"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.xglm.modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				)
@require_tf
class 			_snake_case  :
						snake_case__      	=      XGLMConfig
						snake_case__      	=      {}
						snake_case__      	=      "gelu"






						def __init__(							self   :      Any				,       UpperCAmelCase   :      Optional[int]				,       UpperCAmelCase   :      List[str]=14				,       UpperCAmelCase   :      Optional[int]=7				,       UpperCAmelCase   :      Optional[Any]=True				,       UpperCAmelCase   :      Optional[int]=True				,       UpperCAmelCase   :      List[Any]=True				,       UpperCAmelCase   :      List[str]=99				,       UpperCAmelCase   :      Tuple=32				,       UpperCAmelCase   :      int=2				,       UpperCAmelCase   :      Dict=4				,       UpperCAmelCase   :      str=37				,       UpperCAmelCase   :      Dict="gelu"				,       UpperCAmelCase   :      List[str]=0.1				,       UpperCAmelCase   :      List[str]=0.1				,       UpperCAmelCase   :      Optional[Any]=512				,       UpperCAmelCase   :      Any=0.0_2				,       ):
													__lowerCamelCase       :		Optional[Any]		 =  parent
													__lowerCamelCase       :		Union[str, Any]		 =  batch_size
													__lowerCamelCase       :		Union[str, Any]		 =  seq_length
													__lowerCamelCase       :		Optional[int]		 =  is_training
													__lowerCamelCase       :		Union[str, Any]		 =  use_input_mask
													__lowerCamelCase       :		List[str]		 =  use_labels
													__lowerCamelCase       :		Optional[Any]		 =  vocab_size
													__lowerCamelCase       :		Any		 =  d_model
													__lowerCamelCase       :		Optional[int]		 =  num_hidden_layers
													__lowerCamelCase       :		Optional[int]		 =  num_attention_heads
													__lowerCamelCase       :		Optional[Any]		 =  ffn_dim
													__lowerCamelCase       :		Union[str, Any]		 =  activation_function
													__lowerCamelCase       :		str		 =  activation_dropout
													__lowerCamelCase       :		Tuple		 =  attention_dropout
													__lowerCamelCase       :		Tuple		 =  max_position_embeddings
													__lowerCamelCase       :		int		 =  initializer_range
													__lowerCamelCase       :		Tuple		 =  None
													__lowerCamelCase       :		List[Any]		 =  0
													__lowerCamelCase       :		Dict		 =  2
													__lowerCamelCase       :		List[str]		 =  1






						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													return XGLMConfig.from_pretrained("facebook/xglm-564M"     )






						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													__lowerCamelCase       :		Optional[Any]		 =  tf.clip_by_value(
													    ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size     )				,       clip_value_min=0				,       clip_value_max=3     )

													__lowerCamelCase       :		List[str]		 =  None
													if self.use_input_mask:
																				__lowerCamelCase       :		List[str]		 =  random_attention_mask([self.batch_size, self.seq_length]     )

													__lowerCamelCase       :		str		 =  self.get_config()

													__lowerCamelCase       :		List[Any]		 =  floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,       2     )

													return (
													    config,
													    input_ids,
													    input_mask,
													    head_mask,
													)






						def    lowerCamelCase__  (							self   :      Tuple     ):
													return XGLMConfig(
													    vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       num_layers=self.num_hidden_layers				,       attention_heads=self.num_attention_heads				,       ffn_dim=self.ffn_dim				,       activation_function=self.activation_function				,       activation_dropout=self.activation_dropout				,       attention_dropout=self.attention_dropout				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       use_cache=A_				,       bos_token_id=self.bos_token_id				,       eos_token_id=self.eos_token_id				,       pad_token_id=self.pad_token_id				,       return_dict=A_				,       )







						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													__lowerCamelCase       :		Union[str, Any]		 =  self.prepare_config_and_inputs()

													(
													    (
													    __lowerCamelCase
													) ,	(
													    __lowerCamelCase
													) ,	(
													    __lowerCamelCase
													) ,	(
													    __lowerCamelCase
													) ,	
													)       :		Dict		 =  config_and_inputs

													__lowerCamelCase       :		str		 =  {
													    "input_ids": input_ids,
													    "head_mask": head_mask,
													}

													return config, inputs_dict

@require_tf
class 			_snake_case  (       a__					,			a__					,			unittest.TestCase		):
						snake_case__      	=      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
						snake_case__      	=      (TFXGLMForCausalLM,) if is_tf_available() else ()
						snake_case__      	=      (
						    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
						)
						snake_case__      	=      False
						snake_case__      	=      False
						snake_case__      	=      False






						def    lowerCamelCase__  (							self   :      int     ):
													__lowerCamelCase       :		int		 =  TFXGLMModelTester(self     )
													__lowerCamelCase       :		Tuple		 =  ConfigTester(self				,       config_class=A_				,       n_embd=37     )






						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													self.config_tester.run_common_tests()






						@slow
						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowerCamelCase       :		Any		 =  TFXGLMModel.from_pretrained(A_     )
																				self.assertIsNotNone(A_     )







						@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."     )
						def    lowerCamelCase__  (							self   :      List[str]     ):
													super().test_resize_token_embeddings()

@require_tf
class 			_snake_case  (       unittest.TestCase		):






						@slow
						def    lowerCamelCase__  (							self   :      Optional[Any]				,       UpperCAmelCase   :      int=True     ):
													__lowerCamelCase       :		List[Any]		 =  TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
													__lowerCamelCase       :		List[str]		 =  tf.convert_to_tensor([[2, 268, 9865]]				,       dtype=tf.intaa     )  # The dog
													# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
													# fmt: off
													__lowerCamelCase       :		Tuple		 =  [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
													# fmt: on
													__lowerCamelCase       :		Optional[Any]		 =  model.generate(A_				,       do_sample=A_				,       num_beams=1     )
													if verify_outputs:
																				self.assertListEqual(output_ids[0].numpy().tolist()				,       A_     )






						@slow
						def    lowerCamelCase__  (							self   :      Tuple     ):
													__lowerCamelCase       :		Any		 =  XGLMTokenizer.from_pretrained("facebook/xglm-564M"     )
													__lowerCamelCase       :		Dict		 =  TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )

													tf.random.set_seed(0     )
													__lowerCamelCase       :		Any		 =  tokenizer("Today is a nice day and"				,       return_tensors="tf"     )
													__lowerCamelCase       :		Optional[Any]		 =  tokenized.input_ids
													# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
													with tf.device(":/CPU:0"     ):
																				__lowerCamelCase       :		List[str]		 =  model.generate(A_				,       do_sample=A_				,       seed=[7, 0]     )
													__lowerCamelCase       :		Dict		 =  tokenizer.decode(output_ids[0]				,       skip_special_tokens=A_     )

													__lowerCamelCase       :		List[str]		 =  (
													    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
													)
													self.assertEqual(A_				,       A_     )







						@slow
						def    lowerCamelCase__  (							self   :      str     ):
													__lowerCamelCase       :		Optional[Any]		 =  TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
													__lowerCamelCase       :		List[Any]		 =  XGLMTokenizer.from_pretrained("facebook/xglm-564M"     )

													__lowerCamelCase       :		List[Any]		 =  "left"

													# use different length sentences to test batching
													__lowerCamelCase       :		Any		 =  [
													    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
													    "left-padding, such as in batched generation. The output for the sequence below should be the same "
													    "regardless of whether left padding is applied or not. When",
													    "Hello, my dog is a little",
													]

													__lowerCamelCase       :		str		 =  tokenizer(A_				,       return_tensors="tf"				,       padding=A_     )
													__lowerCamelCase       :		str		 =  inputs["input_ids"]

													__lowerCamelCase       :		Optional[Any]		 =  model.generate(input_ids=A_				,       attention_mask=inputs["attention_mask"]				,       max_new_tokens=12     )

													__lowerCamelCase       :		Union[str, Any]		 =  tokenizer(sentences[0]				,       return_tensors="tf"     ).input_ids
													__lowerCamelCase       :		str		 =  model.generate(input_ids=A_				,       max_new_tokens=12     )

													__lowerCamelCase       :		List[Any]		 =  tokenizer(sentences[1]				,       return_tensors="tf"     ).input_ids
													__lowerCamelCase       :		Optional[int]		 =  model.generate(input_ids=A_				,       max_new_tokens=12     )

													__lowerCamelCase       :		Dict		 =  tokenizer.batch_decode(A_				,       skip_special_tokens=A_     )
													__lowerCamelCase       :		Any		 =  tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=A_     )
													__lowerCamelCase       :		str		 =  tokenizer.decode(output_padded[0]				,       skip_special_tokens=A_     )

													__lowerCamelCase       :		Any		 =  [
													    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
													    "left-padding, such as in batched generation. The output for the sequence below should be the same "
													    "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
													    "a single",
													    "Hello, my dog is a little bit of a shy one, but he is very friendly",
													]
													self.assertListEqual(A_				,       A_     )
													self.assertListEqual(A_				,       [non_padded_sentence, padded_sentence]     )



"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




