






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   :   Optional[Any]							=  XGLMConfig
		_snake_case   :   int							=  {}
		_snake_case   :   Optional[Any]							=  """gelu"""



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_4    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=4    ,		_UpperCamelCase=3_7    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=0.02    ,		)     ->							Optional[Any]:
							UpperCAmelCase_   :				Any						=				parent
							UpperCAmelCase_   :				Tuple						=				batch_size
							UpperCAmelCase_   :				Optional[Any]						=				seq_length
							UpperCAmelCase_   :				Tuple						=				is_training
							UpperCAmelCase_   :				List[str]						=				use_input_mask
							UpperCAmelCase_   :				Union[str, Any]						=				use_labels
							UpperCAmelCase_   :				Union[str, Any]						=				vocab_size
							UpperCAmelCase_   :				str						=				d_model
							UpperCAmelCase_   :				Dict						=				num_hidden_layers
							UpperCAmelCase_   :				str						=				num_attention_heads
							UpperCAmelCase_   :				Any						=				ffn_dim
							UpperCAmelCase_   :				str						=				activation_function
							UpperCAmelCase_   :				Optional[int]						=				activation_dropout
							UpperCAmelCase_   :				str						=				attention_dropout
							UpperCAmelCase_   :				Dict						=				max_position_embeddings
							UpperCAmelCase_   :				Optional[int]						=				initializer_range
							UpperCAmelCase_   :				Optional[int]						=				None
							UpperCAmelCase_   :				Dict						=				0
							UpperCAmelCase_   :				Optional[int]						=				2
							UpperCAmelCase_   :				List[Any]						=				1



		def  __UpperCAmelCase      (							self       )     ->							int:
							return XGLMConfig.from_pretrained('facebook/xglm-564M'       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				str						=				tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size       )    ,		clip_value_min=0    ,		clip_value_max=3       )

							UpperCAmelCase_   :				Union[str, Any]						=				None
							if self.use_input_mask:
												UpperCAmelCase_   :				Optional[Any]						=				random_attention_mask([self.batch_size, self.seq_length]       )

							UpperCAmelCase_   :				Union[str, Any]						=				self.get_config()

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

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							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=__lowercase    ,		bos_token_id=self.bos_token_id    ,		eos_token_id=self.eos_token_id    ,		pad_token_id=self.pad_token_id    ,		return_dict=__lowercase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Tuple						=				self.prepare_config_and_inputs()

							(
							    UpperCAmelCase_			
							)   :				Optional[int]						=				config_and_inputs

							UpperCAmelCase_   :				Optional[Any]						=				{
							    '''input_ids''': input_ids,
							    '''head_mask''': head_mask,
							}

							return config, inputs_dict





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







		'''simple docstring'''



		_snake_case   :   str							=  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
		_snake_case   :   List[Any]							=  (TFXGLMForCausalLM,) if is_tf_available() else ()
		_snake_case   :   Optional[int]							=  (
		    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
		)
		_snake_case   :   Optional[int]							=  False
		_snake_case   :   Optional[Any]							=  False
		_snake_case   :   str							=  False



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=				TFXGLMModelTester(self       )
							UpperCAmelCase_   :				Union[str, Any]						=				ConfigTester(self    ,		config_class=__lowercase    ,		n_embd=3_7       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							self.config_tester.run_common_tests()



		@slow
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_   :				Optional[Any]						=				TFXGLMModel.from_pretrained(__lowercase       )
												self.assertIsNotNone(__lowercase       )



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





@require_tf
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		@slow
		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase=True       )     ->							List[Any]:
							UpperCAmelCase_   :				List[Any]						=				TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )
							UpperCAmelCase_   :				Any						=				tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]    ,		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
							UpperCAmelCase_   :				Any						=				[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
							# fmt: on
							UpperCAmelCase_   :				Union[str, Any]						=				model.generate(__lowercase    ,		do_sample=__lowercase    ,		num_beams=1       )
							if verify_outputs:
												self.assertListEqual(output_ids[0].numpy().tolist()    ,		__lowercase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Optional[int]						=				XGLMTokenizer.from_pretrained('facebook/xglm-564M'       )
							UpperCAmelCase_   :				str						=				TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )

							tf.random.set_seed(0       )
							UpperCAmelCase_   :				Optional[Any]						=				tokenizer('Today is a nice day and'    ,		return_tensors='tf'       )
							UpperCAmelCase_   :				Union[str, 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'       ):
												UpperCAmelCase_   :				List[str]						=				model.generate(__lowercase    ,		do_sample=__lowercase    ,		seed=[7, 0]       )
							UpperCAmelCase_   :				List[str]						=				tokenizer.decode(output_ids[0]    ,		skip_special_tokens=__lowercase       )

							UpperCAmelCase_   :				int						=				(
							    '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
							)
							self.assertEqual(__lowercase    ,		__lowercase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[Any]						=				TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )
							UpperCAmelCase_   :				Dict						=				XGLMTokenizer.from_pretrained('facebook/xglm-564M'       )

							UpperCAmelCase_   :				Optional[int]						=				'''left'''

							# use different length sentences to test batching
							UpperCAmelCase_   :				List[str]						=				[
							    '''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''',
							]

							UpperCAmelCase_   :				Optional[Any]						=				tokenizer(__lowercase    ,		return_tensors='tf'    ,		padding=__lowercase       )
							UpperCAmelCase_   :				Any						=				inputs['''input_ids''']

							UpperCAmelCase_   :				List[Any]						=				model.generate(input_ids=__lowercase    ,		attention_mask=inputs['attention_mask']    ,		max_new_tokens=1_2       )

							UpperCAmelCase_   :				Tuple						=				tokenizer(sentences[0]    ,		return_tensors='tf'       ).input_ids
							UpperCAmelCase_   :				Optional[Any]						=				model.generate(input_ids=__lowercase    ,		max_new_tokens=1_2       )

							UpperCAmelCase_   :				Any						=				tokenizer(sentences[1]    ,		return_tensors='tf'       ).input_ids
							UpperCAmelCase_   :				Any						=				model.generate(input_ids=__lowercase    ,		max_new_tokens=1_2       )

							UpperCAmelCase_   :				Optional[Any]						=				tokenizer.batch_decode(__lowercase    ,		skip_special_tokens=__lowercase       )
							UpperCAmelCase_   :				Union[str, Any]						=				tokenizer.decode(output_non_padded[0]    ,		skip_special_tokens=__lowercase       )
							UpperCAmelCase_   :				List[str]						=				tokenizer.decode(output_padded[0]    ,		skip_special_tokens=__lowercase       )

							UpperCAmelCase_   :				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 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(__lowercase    ,		__lowercase       )
							self.assertListEqual(__lowercase    ,		[non_padded_sentence, padded_sentence]       )














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) < k or k < 0:
										raise ValueError('Invalid Input'  )
					UpperCAmelCase_   :				int						=						sum(array[:k]  )
					for i in range(len(__snake_case  ) - k  ):
										UpperCAmelCase_   :				List[Any]						=						current_sum - array[i] + array[i + k]
										UpperCAmelCase_   :				List[Any]						=						max(__snake_case					,	__snake_case  )
					return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		__UpperCAmelCase     							=						[randint(-1000, 1000) for i in range(100)]
		__UpperCAmelCase     							=						randint(0, 110)
		print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')






