





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        _a:







		def __init__(    self ,						__snake_case ,						)      ->      int:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   parent
							_snake_case       :					Union[str, Any]				       =   1_3
							_snake_case       :					List[Any]				       =   7
							_snake_case       :					Dict				       =   3_0
							_snake_case       :					Optional[Any]				       =   self.seq_length + self.mem_len
							_snake_case       :					Optional[int]				       =   1_5
							_snake_case       :					Any				       =   True
							_snake_case       :					str				       =   True
							_snake_case       :					Optional[int]				       =   9_9
							_snake_case       :					int				       =   [1_0, 5_0, 8_0]
							_snake_case       :					List[str]				       =   3_2
							_snake_case       :					Tuple				       =   3_2
							_snake_case       :					Union[str, Any]				       =   4
							_snake_case       :					Union[str, Any]				       =   8
							_snake_case       :					List[str]				       =   1_2_8
							_snake_case       :					Union[str, Any]				       =   2
							_snake_case       :					List[str]				       =   2
							_snake_case       :					Dict				       =   None
							_snake_case       :					Any				       =   1
							_snake_case       :					Optional[Any]				       =   0
							_snake_case       :					Optional[Any]				       =   3
							_snake_case       :					List[str]				       =   self.vocab_size - 1
							_snake_case       :					Optional[int]				       =   0.01







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)
							_snake_case       :					int				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)

							_snake_case       :					Tuple				       =   None
							if self.use_labels:
												_snake_case       :					List[str]				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)

							_snake_case       :					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        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   TFTransfoXLModel(__snake_case				)

							_snake_case       :					List[Any]				       =   model(__snake_case				).to_tuple()

							_snake_case       :					Dict				       =   {"input_ids": input_ids_a, "mems": mems_a}

							_snake_case       :					Optional[Any]				       =   model(__snake_case				).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 ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   TFTransfoXLLMHeadModel(__snake_case				)

							_snake_case       :					Optional[int]				       =   model(__snake_case				).to_tuple()

							_snake_case       :					Tuple				       =   {"input_ids": input_ids_a, "labels": lm_labels}
							_snake_case       :					Optional[int]				       =   model(__snake_case				).to_tuple()

							_snake_case       :					Optional[Any]				       =   model([input_ids_a, mems_a]				).to_tuple()

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

							_snake_case       :					List[Any]				       =   model(__snake_case				).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 ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   TFTransfoXLForSequenceClassification(__snake_case				)
							_snake_case       :					Tuple				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					str				       =   self.prepare_config_and_inputs()
							(_snake_case)       :					Dict				       =   config_and_inputs
							_snake_case       :					Optional[int]				       =   {"input_ids": input_ids_a}
							return config, inputs_dict






@require_tf
class        _a(    __A	,   __A	,   unittest.TestCase    ):
		lowerCamelCase__     :str   				=		(
		    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
		)
		lowerCamelCase__     :Optional[Any]   				=		() if is_tf_available() else ()
		lowerCamelCase__     :List[str]   				=		(
		    {
		        '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__     :List[str]   				=		False
		lowerCamelCase__     :List[Any]   				=		False
		lowerCamelCase__     :Any   				=		False
		lowerCamelCase__     :List[str]   				=		False







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      List[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				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					int				       =   TFTransfoXLModelTester(self				)
							_snake_case       :					Tuple				       =   ConfigTester(self ,						config_class=__snake_case ,						d_embed=3_7				)







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


							'''simple docstring'''

							self.config_tester.run_common_tests()







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


							'''simple docstring'''

							self.model_tester.set_seed()
							_snake_case       :					Tuple				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_transfo_xl_model(*__snake_case				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							self.model_tester.set_seed()
							_snake_case       :					Any				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_transfo_xl_lm_head(*__snake_case				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__snake_case				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.model_tester.prepare_config_and_inputs_for_common()
							_snake_case       :					Union[str, Any]				       =   [TFTransfoXLForSequenceClassification]

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







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							pass







		@slow
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_snake_case       :					Union[str, Any]				       =   TFTransfoXLModel.from_pretrained(__snake_case				)
												self.assertIsNotNone(__snake_case				)







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


							'''simple docstring'''

							pass






@require_tf
class        _a(    unittest.TestCase    ):







		@unittest.skip("Skip test until #12651 is resolved."				)
		@slow
		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"				)
							# fmt: off
							_snake_case       :					Union[str, Any]				       =   tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] ,						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       :					Tuple				       =   [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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       :					List[str]				       =   model.generate(__snake_case ,						max_length=2_0_0 ,						do_sample=__snake_case				)
							self.assertListEqual(output_ids[0].numpy().tolist() ,						__snake_case				)






import re







def    A				(							UpperCAmelCase				):
					_snake_case       :					Any				       =   re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"				)
					if match := re.search(UpperCAmelCase  ,  UpperCAmelCase				):
										return match.string == phone
					return False


if __name__ == "__main__":
							print(indian_phone_validator('+918827897895'))