
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      _SCREAMING_SNAKE_CASE				:
				lowerCamelCase_	  =  XGLMConfig
				lowerCamelCase_	  =  {}
				lowerCamelCase_	  =  'gelu'





				def __init__(		self			:		Dict       ,   snake_case_			:		str       ,   snake_case_			:		int=14       ,   snake_case_			:		str=7       ,   snake_case_			:		Any=True       ,   snake_case_			:		Optional[int]=True       ,   snake_case_			:		List[Any]=True       ,   snake_case_			:		List[str]=99       ,   snake_case_			:		Union[str, Any]=32       ,   snake_case_			:		List[Any]=2       ,   snake_case_			:		List[Any]=4       ,   snake_case_			:		str=37       ,   snake_case_			:		Tuple="gelu"       ,   snake_case_			:		List[str]=0.1       ,   snake_case_			:		int=0.1       ,   snake_case_			:		Optional[int]=512       ,   snake_case_			:		Optional[int]=0.02       ,   ):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								A  :	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				)

								A  :	Optional[int]				=					None
								if self.use_input_mask:
												A  :	Any				=					random_attention_mask([self.batch_size, self.seq_length]				)

								A  :	str				=					self.get_config()

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

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								)





				def 							_UpperCAmelCase    (		self			:		List[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=SCREAMING_SNAKE_CASE_       ,   bos_token_id=self.bos_token_id       ,   eos_token_id=self.eos_token_id       ,   pad_token_id=self.pad_token_id       ,   return_dict=SCREAMING_SNAKE_CASE_       ,   )






				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	List[Any]				=					self.prepare_config_and_inputs()

								(
								    A			
								)  :	str				=					config_and_inputs

								A  :	Union[str, Any]				=					{
								    'input_ids': input_ids,
								    'head_mask': head_mask,
								}

								return config, inputs_dict



@require_tf
class      _SCREAMING_SNAKE_CASE				(		_UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase				):
				lowerCamelCase_	  =  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				lowerCamelCase_	  =  (TFXGLMForCausalLM,) if is_tf_available() else ()
				lowerCamelCase_	  =  (
				    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
				)
				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	str				=					TFXGLMModelTester(self				)
								A  :	List[Any]				=					ConfigTester(self       ,   config_class=SCREAMING_SNAKE_CASE_       ,   n_embd=37				)





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								self.config_tester.run_common_tests()





				@slow
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A  :	Optional[Any]				=					TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_				)
												self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)






				@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''				)
				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								super().test_resize_token_embeddings()



@require_tf
class      _SCREAMING_SNAKE_CASE				(		unittest.TestCase				):





				@slow
				def 							_UpperCAmelCase    (		self			:		List[str]       ,   snake_case_			:		int=True				):


								"""simple docstring"""

								A  :	Optional[Any]				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''				)
								A  :	int				=					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
								A  :	Optional[int]				=					[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
								# fmt: on
								A  :	Any				=					model.generate(SCREAMING_SNAKE_CASE_       ,   do_sample=SCREAMING_SNAKE_CASE_       ,   num_beams=1				)
								if verify_outputs:
												self.assertListEqual(output_ids[0].numpy().tolist()       ,   SCREAMING_SNAKE_CASE_				)





				@slow
				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								A  :	Any				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''				)
								A  :	Tuple				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''				)

								tf.random.set_seed(0				)
								A  :	List[Any]				=					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  :	Tuple				=					model.generate(SCREAMING_SNAKE_CASE_       ,   do_sample=SCREAMING_SNAKE_CASE_       ,   seed=[7, 0]				)
								A  :	Optional[Any]				=					tokenizer.decode(output_ids[0]       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_				)

								A  :	Union[str, Any]				=					(
								    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
								)
								self.assertEqual(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_				)






				@slow
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								A  :	Tuple				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''				)
								A  :	Any				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''				)

								A  :	Any				=					'left'

								# use different length sentences to test batching
								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',
								    'Hello, my dog is a little',
								]

								A  :	Any				=					tokenizer(SCREAMING_SNAKE_CASE_       ,   return_tensors='''tf'''       ,   padding=SCREAMING_SNAKE_CASE_				)
								A  :	Optional[int]				=					inputs['input_ids']

								A  :	str				=					model.generate(input_ids=SCREAMING_SNAKE_CASE_       ,   attention_mask=inputs['''attention_mask''']       ,   max_new_tokens=12				)

								A  :	Optional[int]				=					tokenizer(sentences[0]       ,   return_tensors='''tf'''				).input_ids
								A  :	int				=					model.generate(input_ids=SCREAMING_SNAKE_CASE_       ,   max_new_tokens=12				)

								A  :	Optional[Any]				=					tokenizer(sentences[1]       ,   return_tensors='''tf'''				).input_ids
								A  :	Optional[Any]				=					model.generate(input_ids=SCREAMING_SNAKE_CASE_       ,   max_new_tokens=12				)

								A  :	Union[str, Any]				=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_				)
								A  :	Union[str, Any]				=					tokenizer.decode(output_non_padded[0]       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_				)
								A  :	Union[str, Any]				=					tokenizer.decode(output_padded[0]       ,   skip_special_tokens=SCREAMING_SNAKE_CASE_				)

								A  :	Optional[int]				=					[
								    '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(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_				)
								self.assertListEqual(SCREAMING_SNAKE_CASE_       ,   [non_padded_sentence, padded_sentence]				)



'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 10_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						3
     __lowerCamelCase  :      Dict														=						0
     while a < n:
          if a % 3 == 0 or a % 5 == 0:
               result += a
          elif a % 15 == 0:
               result -= a
          a += 1
     return result


if __name__ == "__main__":
      print(f'''{solution() = }''')






