



"""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__    :       Any        =						XGLMConfig
		snake_case__    :       Dict        =						{}
		snake_case__    :       Optional[int]        =						'gelu'




		def __init__(			self					:						Optional[int]						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						Union[str, Any]=1_4						, __lowerCAmelCase					:						List[str]=7						, __lowerCAmelCase					:						int=True						, __lowerCAmelCase					:						Dict=True						, __lowerCAmelCase					:						int=True						, __lowerCAmelCase					:						Tuple=9_9						, __lowerCAmelCase					:						List[str]=3_2						, __lowerCAmelCase					:						Dict=2						, __lowerCAmelCase					:						Optional[int]=4						, __lowerCAmelCase					:						Optional[int]=3_7						, __lowerCAmelCase					:						Dict="gelu"						, __lowerCAmelCase					:						List[str]=0.1						, __lowerCAmelCase					:						List[Any]=0.1						, __lowerCAmelCase					:						List[str]=5_1_2						, __lowerCAmelCase					:						str=0.02						, ):






									"""simple docstring"""






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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






									"""simple docstring"""






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




		def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






									"""simple docstring"""






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

									_lowerCamelCase						:    Optional[Any]         =			self.get_config()

									_lowerCamelCase						:    Union[str, Any]         =			floats_tensor([self.num_hidden_layers, self.num_attention_heads]						, 2		)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)




		def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






									"""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 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






									"""simple docstring"""






									_lowerCamelCase						:    int         =			self.prepare_config_and_inputs()

									(
									    _lowerCamelCase    
									)						:    str         =			config_and_inputs

									_lowerCamelCase						:    Union[str, Any]         =			{
									    "input_ids": input_ids,
									    "head_mask": head_mask,
									}

									return config, inputs_dict






@require_tf
class 	__snake_case	(     SCREAMING_SNAKE_CASE__  ,					SCREAMING_SNAKE_CASE__  ,					unittest.TestCase):
		snake_case__    :       Optional[int]        =						(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
		snake_case__    :       Optional[Any]        =						(TFXGLMForCausalLM,) if is_tf_available() else ()
		snake_case__    :       Any        =						(
		    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
		)
		snake_case__    :       int        =						False
		snake_case__    :       str        =						False
		snake_case__    :       Union[str, Any]        =						False




		def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






									"""simple docstring"""






									_lowerCamelCase						:    Dict         =			TFXGLMModelTester(self		)
									_lowerCamelCase						:    List[Any]         =			ConfigTester(self						, config_class=_SCREAMING_SNAKE_CASE						, n_embd=3_7		)




		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






									"""simple docstring"""






									self.config_tester.run_common_tests()




		@slow
		def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






									"""simple docstring"""






									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_lowerCamelCase						:    List[str]         =			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 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






									"""simple docstring"""






									super().test_resize_token_embeddings()






@require_tf
class 	__snake_case	(     unittest.TestCase):




		@slow
		def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						Any=True		):






									"""simple docstring"""






									_lowerCamelCase						:    Union[str, Any]         =			TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''		)
									_lowerCamelCase						:    str         =			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
									_lowerCamelCase						:    Optional[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
									_lowerCamelCase						:    Optional[int]         =			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 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






									"""simple docstring"""






									_lowerCamelCase						:    str         =			XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''		)
									_lowerCamelCase						:    Union[str, Any]         =			TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''		)

									tf.random.set_seed(0		)
									_lowerCamelCase						:    str         =			tokenizer('''Today is a nice day and'''						, return_tensors='''tf'''		)
									_lowerCamelCase						:    List[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(_SCREAMING_SNAKE_CASE						, do_sample=_SCREAMING_SNAKE_CASE						, seed=[7, 0]		)
									_lowerCamelCase						:    int         =			tokenizer.decode(output_ids[0]						, skip_special_tokens=_SCREAMING_SNAKE_CASE		)

									_lowerCamelCase						:    Dict         =			(
									    "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 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






									"""simple docstring"""






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

									_lowerCamelCase						:    Any         =			"left"

									# use different length sentences to test batching
									_lowerCamelCase						:    Dict         =			[
									    "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						:    Dict         =			tokenizer(_SCREAMING_SNAKE_CASE						, return_tensors='''tf'''						, padding=_SCREAMING_SNAKE_CASE		)
									_lowerCamelCase						:    int         =			inputs["input_ids"]

									_lowerCamelCase						:    Tuple         =			model.generate(input_ids=_SCREAMING_SNAKE_CASE						, attention_mask=inputs['''attention_mask''']						, max_new_tokens=1_2		)

									_lowerCamelCase						:    int         =			tokenizer(sentences[0]						, return_tensors='''tf'''		).input_ids
									_lowerCamelCase						:    Dict         =			model.generate(input_ids=_SCREAMING_SNAKE_CASE						, max_new_tokens=1_2		)

									_lowerCamelCase						:    int         =			tokenizer(sentences[1]						, return_tensors='''tf'''		).input_ids
									_lowerCamelCase						:    Tuple         =			model.generate(input_ids=_SCREAMING_SNAKE_CASE						, max_new_tokens=1_2		)

									_lowerCamelCase						:    Optional[Any]         =			tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						, skip_special_tokens=_SCREAMING_SNAKE_CASE		)
									_lowerCamelCase						:    Dict         =			tokenizer.decode(output_non_padded[0]						, skip_special_tokens=_SCREAMING_SNAKE_CASE		)
									_lowerCamelCase						:    Tuple         =			tokenizer.decode(output_padded[0]						, skip_special_tokens=_SCREAMING_SNAKE_CASE		)

									_lowerCamelCase						:    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"""






from itertools import permutations






def 	snake_case_  (				A_ :		tuple						):


       '''simple docstring'''





       if num[3] % 2 != 0:
              return False

       if (num[2] + num[3] + num[4]) % 3 != 0:
              return False

       if num[5] % 5 != 0:
              return False

       _lowerCamelCase						:    Any         =			[7, 11, 13, 17]
       for i, test in enumerate(A_						):
              if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                     return False
       return True






def 	snake_case_  (				A_ :		int = 10						):


       '''simple docstring'''





       return sum(
           int(''''''.join(map(A_,    A_						)						)						)
           for num in permutations(range(A_						)						)
           if is_substring_divisible(A_						)						)


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


