

'''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 lowerCamelCase			:



			'''simple docstring'''






			__snake_case        =	XGLMConfig
			__snake_case        =	{}
			__snake_case        =	'gelu'


			def __init__(				self	:						str		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[str]=14		,      lowerCAmelCase_	:						Optional[Any]=7		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						Any=True		,      lowerCAmelCase_	:						Tuple=True		,      lowerCAmelCase_	:						Union[str, Any]=99		,      lowerCAmelCase_	:						Union[str, Any]=32		,      lowerCAmelCase_	:						Dict=2		,      lowerCAmelCase_	:						int=4		,      lowerCAmelCase_	:						int=37		,      lowerCAmelCase_	:						int="gelu"		,      lowerCAmelCase_	:						Optional[Any]=0.1		,      lowerCAmelCase_	:						Optional[int]=0.1		,      lowerCAmelCase_	:						Any=5_12		,      lowerCAmelCase_	:						List[Any]=0.02		,      )      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       List[str]     =parent
									A__					:       str     =batch_size
									A__					:       List[str]     =seq_length
									A__					:       Optional[Any]     =is_training
									A__					:       Tuple     =use_input_mask
									A__					:       str     =use_labels
									A__					:       List[str]     =vocab_size
									A__					:       Tuple     =d_model
									A__					:       Tuple     =num_hidden_layers
									A__					:       str     =num_attention_heads
									A__					:       Any     =ffn_dim
									A__					:       int     =activation_function
									A__					:       Union[str, Any]     =activation_dropout
									A__					:       int     =attention_dropout
									A__					:       Any     =max_position_embeddings
									A__					:       List[str]     =initializer_range
									A__					:       List[Any]     =None
									A__					:       Tuple     =0
									A__					:       Optional[Any]     =2
									A__					:       List[Any]     =1


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





									'''simple docstring'''
									return XGLMConfig.from_pretrained("""facebook/xglm-564M"""			)


			def lowercase__						(				self	:						Any			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[int]     =tf.clip_by_value(
									    ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size			)		,      clip_value_min=0		,      clip_value_max=3			)

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

									A__					:       Dict     =self.get_config()

									A__					:       Optional[int]     =floats_tensor([self.num_hidden_layers, self.num_attention_heads]		,      2			)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)


			def lowercase__						(				self	:						Optional[Any]			)      ->		str:





									'''simple docstring'''
									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=lowerCAmelCase_		,      bos_token_id=self.bos_token_id		,      eos_token_id=self.eos_token_id		,      pad_token_id=self.pad_token_id		,      return_dict=lowerCAmelCase_		,      )



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





									'''simple docstring'''
									A__					:       Tuple     =self.prepare_config_and_inputs()

									(
									    (
									    A__
									)   ,						(
									    A__
									)   ,						(
									    A__
									)   ,						(
									    A__
									)   ,						
									)					:       Union[str, Any]     =config_and_inputs

									A__					:       Optional[int]     ={
									    """input_ids""": input_ids,
									    """head_mask""": head_mask,
									}

									return config, inputs_dict



@require_tf
class lowerCamelCase			(  lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__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 lowercase__						(				self	:						Any			)      ->		int:





									'''simple docstring'''
									A__					:       Union[str, Any]     =TFXGLMModelTester(self			)
									A__					:       List[str]     =ConfigTester(self		,      config_class=lowerCAmelCase_		,      n_embd=37			)


			def lowercase__						(				self	:						List[str]			)      ->		int:





									'''simple docstring'''
									self.config_tester.run_common_tests()


			@slow
			def lowercase__						(				self	:						Any			)      ->		Optional[Any]:





									'''simple docstring'''
									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															A__					:       int     =TFXGLMModel.from_pretrained(lowerCAmelCase_			)
															self.assertIsNotNone(lowerCAmelCase_			)



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





									'''simple docstring'''
									super().test_resize_token_embeddings()



@require_tf
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			@slow
			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Optional[Any]=True			)      ->		int:





									'''simple docstring'''
									A__					:       str     =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
									A__					:       Tuple     =tf.convert_to_tensor([[2, 2_68, 98_65]]		,      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
									A__					:       List[str]     =[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
									# fmt: on
									A__					:       Optional[int]     =model.generate(lowerCAmelCase_		,      do_sample=lowerCAmelCase_		,      num_beams=1			)
									if verify_outputs:
															self.assertListEqual(output_ids[0].numpy().tolist()		,      lowerCAmelCase_			)


			@slow
			def lowercase__						(				self	:						List[Any]			)      ->		int:





									'''simple docstring'''
									A__					:       List[str]     =XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""			)
									A__					:       List[Any]     =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)

									tf.random.set_seed(0			)
									A__					:       Dict     =tokenizer("""Today is a nice day and"""		,      return_tensors="""tf"""			)
									A__					:       int     =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"""			):
															A__					:       List[str]     =model.generate(lowerCAmelCase_		,      do_sample=lowerCAmelCase_		,      seed=[7, 0]			)
									A__					:       List[str]     =tokenizer.decode(output_ids[0]		,      skip_special_tokens=lowerCAmelCase_			)

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



			@slow
			def lowercase__						(				self	:						Union[str, Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Optional[int]     =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
									A__					:       str     =XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""			)

									A__					:       Optional[int]     ="""left"""

									# use different length sentences to test batching
									A__					:       Tuple     =[
									    """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""",
									]

									A__					:       Dict     =tokenizer(lowerCAmelCase_		,      return_tensors="""tf"""		,      padding=lowerCAmelCase_			)
									A__					:       Optional[int]     =inputs["""input_ids"""]

									A__					:       Union[str, Any]     =model.generate(input_ids=lowerCAmelCase_		,      attention_mask=inputs["""attention_mask"""]		,      max_new_tokens=12			)

									A__					:       str     =tokenizer(sentences[0]		,      return_tensors="""tf"""			).input_ids
									A__					:       str     =model.generate(input_ids=lowerCAmelCase_		,      max_new_tokens=12			)

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

									A__					:       str     =tokenizer.batch_decode(lowerCAmelCase_		,      skip_special_tokens=lowerCAmelCase_			)
									A__					:       Tuple     =tokenizer.decode(output_non_padded[0]		,      skip_special_tokens=lowerCAmelCase_			)
									A__					:       int     =tokenizer.decode(output_padded[0]		,      skip_special_tokens=lowerCAmelCase_			)

									A__					:       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(lowerCAmelCase_		,      lowerCAmelCase_			)
									self.assertListEqual(lowerCAmelCase_		,      [non_padded_sentence, padded_sentence]			)




'''simple docstring'''



def 						__lowerCamelCase	(       __snake_case					:		int	)	->							bool:






						"""simple docstring"""





						if p < 2:
												raise ValueError("""p should not be less than 2!"""	)
						elif p == 2:
												return True

						A__					:       Any     =4
						A__					:       int     =(1 << p) - 1
						for _ in range(p - 2	):
												A__					:       Dict     =((s * s) - 2) % m
						return s == 0


if __name__ == "__main__":
		print(lucas_lehmer_test(7))
		print(lucas_lehmer_test(11))

