


'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			A__:   Union[str, Any]    =	'''platform'''

			import jax
			import jax.numpy as jnp

			from transformers import MarianTokenizer
			from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     Dict   ,_UpperCAmelCase     :     Union[str, Any]=None   ,_UpperCAmelCase     :     Optional[Any]=None   ,_UpperCAmelCase     :     Tuple=None   ,_UpperCAmelCase     :     Union[str, Any]=None   ,_UpperCAmelCase     :     Any=None   ,_UpperCAmelCase     :     Union[str, Any]=None   ,)  ->      Any:





							if attention_mask is None:
														_a   :			Optional[Any]					 =np.where(input_ids != config.pad_token_id   ,1   ,0  )
							if decoder_attention_mask is None:
														_a   :			Any					 =np.where(decoder_input_ids != config.pad_token_id   ,1   ,0  )
							if head_mask is None:
														_a   :			List[str]					 =np.ones((config.encoder_layers, config.encoder_attention_heads)  )
							if decoder_head_mask is None:
														_a   :			Optional[int]					 =np.ones((config.decoder_layers, config.decoder_attention_heads)  )
							if cross_attn_head_mask is None:
														_a   :			Union[str, Any]					 =np.ones((config.decoder_layers, config.decoder_attention_heads)  )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}







class A__	:






						def __init__(			self      :List[str]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]=1_3					,						SCREAMING_SNAKE_CASE      :str=7					,						SCREAMING_SNAKE_CASE      :Dict=True					,						SCREAMING_SNAKE_CASE      :Optional[int]=False					,						SCREAMING_SNAKE_CASE      :Optional[Any]=9_9					,						SCREAMING_SNAKE_CASE      :str=1_6					,						SCREAMING_SNAKE_CASE      :int=2					,						SCREAMING_SNAKE_CASE      :Optional[Any]=4					,						SCREAMING_SNAKE_CASE      :str=4					,						SCREAMING_SNAKE_CASE      :Dict="gelu"					,						SCREAMING_SNAKE_CASE      :Optional[int]=0.1					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0.1					,						SCREAMING_SNAKE_CASE      :Tuple=3_2					,						SCREAMING_SNAKE_CASE      :Tuple=2					,						SCREAMING_SNAKE_CASE      :Optional[int]=1					,						SCREAMING_SNAKE_CASE      :Tuple=0					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0.02					,						)	-> Optional[Any]:




													'''simple docstring'''
													_a   :			Union[str, Any]					 =parent
													_a   :			Optional[Any]					 =batch_size
													_a   :			int					 =seq_length
													_a   :			List[Any]					 =is_training
													_a   :			Optional[int]					 =use_labels
													_a   :			List[Any]					 =vocab_size
													_a   :			int					 =hidden_size
													_a   :			Dict					 =num_hidden_layers
													_a   :			List[Any]					 =num_attention_heads
													_a   :			List[str]					 =intermediate_size
													_a   :			Optional[int]					 =hidden_act
													_a   :			str					 =hidden_dropout_prob
													_a   :			int					 =attention_probs_dropout_prob
													_a   :			Tuple					 =max_position_embeddings
													_a   :			int					 =eos_token_id
													_a   :			Any					 =pad_token_id
													_a   :			Optional[Any]					 =bos_token_id
													_a   :			List[str]					 =initializer_range






						def __UpperCAmelCase	(			self      :List[str]     )	-> str:




													'''simple docstring'''
													_a   :			int					 =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,						self.vocab_size     )					,						3					,						self.vocab_size     )
													_a   :			Tuple					 =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,						dtype=np.intaa     ))					,						-1     )

													_a   :			Optional[int]					 =shift_tokens_right(SCREAMING_SNAKE_CASE					,						1					,						2     )

													_a   :			List[str]					 =MarianConfig(
													    vocab_size=self.vocab_size					,						d_model=self.hidden_size					,						encoder_layers=self.num_hidden_layers					,						decoder_layers=self.num_hidden_layers					,						encoder_attention_heads=self.num_attention_heads					,						decoder_attention_heads=self.num_attention_heads					,						encoder_ffn_dim=self.intermediate_size					,						decoder_ffn_dim=self.intermediate_size					,						dropout=self.hidden_dropout_prob					,						attention_dropout=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						eos_token_id=self.eos_token_id					,						bos_token_id=self.bos_token_id					,						pad_token_id=self.pad_token_id					,						initializer_range=self.initializer_range					,						use_cache=SCREAMING_SNAKE_CASE					,						)
													_a   :			Optional[Any]					 =prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
													return config, inputs_dict






						def __UpperCAmelCase	(			self      :Dict     )	-> Any:




													'''simple docstring'''
													_a ,   _a   :			str					 =self.prepare_config_and_inputs()
													return config, inputs_dict






						def __UpperCAmelCase	(			self      :List[str]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :Any     )	-> Optional[int]:




													'''simple docstring'''
													_a   :			Optional[Any]					 =2_0
													_a   :			List[Any]					 =model_class_name(SCREAMING_SNAKE_CASE     )

													_a   :			List[str]					 =model.encode(inputs_dict["""input_ids"""]     )

													_a ,   _a   :			int					 =(
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													_a   :			List[str]					 =model.init_cache(decoder_input_ids.shape[0]					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
													_a   :			List[Any]					 =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,						dtype="""i4"""     )

													_a   :			List[str]					 =jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,						)
													_a   :			Optional[int]					 =model.decode(
													    decoder_input_ids[:, :-1]					,						SCREAMING_SNAKE_CASE					,						decoder_attention_mask=SCREAMING_SNAKE_CASE					,						past_key_values=SCREAMING_SNAKE_CASE					,						decoder_position_ids=SCREAMING_SNAKE_CASE					,						)

													_a   :			Optional[int]					 =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype="""i4"""     )
													_a   :			str					 =model.decode(
													    decoder_input_ids[:, -1:]					,						SCREAMING_SNAKE_CASE					,						decoder_attention_mask=SCREAMING_SNAKE_CASE					,						past_key_values=outputs_cache.past_key_values					,						decoder_position_ids=SCREAMING_SNAKE_CASE					,						)

													_a   :			Optional[Any]					 =model.decode(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )

													_a   :			List[Any]					 =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
													self.parent.assertTrue(diff < 1e-3					,						msg=f"Max diff is {diff}"     )






						def __UpperCAmelCase	(			self      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> str:




													'''simple docstring'''
													_a   :			str					 =2_0
													_a   :			Tuple					 =model_class_name(SCREAMING_SNAKE_CASE     )

													_a   :			Union[str, Any]					 =model.encode(inputs_dict["""input_ids"""]     )

													_a ,   _a   :			Optional[int]					 =(
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													_a   :			List[str]					 =jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
													    ]					,						axis=-1					,						)

													_a   :			Dict					 =model.init_cache(decoder_input_ids.shape[0]					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
													_a   :			Any					 =jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,						)

													_a   :			List[str]					 =model.decode(
													    decoder_input_ids[:, :-1]					,						SCREAMING_SNAKE_CASE					,						decoder_attention_mask=SCREAMING_SNAKE_CASE					,						past_key_values=SCREAMING_SNAKE_CASE					,						decoder_position_ids=SCREAMING_SNAKE_CASE					,						)
													_a   :			str					 =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype="""i4"""     )
													_a   :			int					 =model.decode(
													    decoder_input_ids[:, -1:]					,						SCREAMING_SNAKE_CASE					,						past_key_values=outputs_cache.past_key_values					,						decoder_attention_mask=SCREAMING_SNAKE_CASE					,						decoder_position_ids=SCREAMING_SNAKE_CASE					,						)

													_a   :			Dict					 =model.decode(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						decoder_attention_mask=SCREAMING_SNAKE_CASE     )

													_a   :			Tuple					 =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
													self.parent.assertTrue(diff < 1e-3					,						msg=f"Max diff is {diff}"     )







@require_flax
class A__	(						UpperCAmelCase__			,					unittest.TestCase			,					UpperCAmelCase__				):
						__UpperCamelCase	:  List[str]     	=							True
						__UpperCamelCase	:  Optional[int]     	=							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						__UpperCamelCase	:  int     	=							(FlaxMarianMTModel,) if is_flax_available() else ()






						def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[Any]:




													'''simple docstring'''
													_a   :			int					 =FlaxMarianModelTester(self     )






						def __UpperCAmelCase	(			self      :Any     )	-> List[Any]:




													'''simple docstring'''
													_a ,   _a   :			Optional[int]					 =self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






						def __UpperCAmelCase	(			self      :int     )	-> Dict:




													'''simple docstring'''
													_a ,   _a   :			str					 =self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






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




													'''simple docstring'''
													_a ,   _a   :			Dict					 =self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__     ):
																											_a   :			int					 =self._prepare_for_class(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
																											_a   :			Dict					 =model_class(SCREAMING_SNAKE_CASE     )

																											@jax.jit
																											def encode_jitted(SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :str=None					,						**SCREAMING_SNAKE_CASE      :Dict     ):
																																		return model.encode(input_ids=SCREAMING_SNAKE_CASE					,						attention_mask=SCREAMING_SNAKE_CASE     )

																											with self.subTest("""JIT Enabled"""     ):
																																		_a   :			Union[str, Any]					 =encode_jitted(**SCREAMING_SNAKE_CASE     ).to_tuple()

																											with self.subTest("""JIT Disabled"""     ):
																																		with jax.disable_jit():
																																									_a   :			Dict					 =encode_jitted(**SCREAMING_SNAKE_CASE     ).to_tuple()

																											self.assertEqual(len(SCREAMING_SNAKE_CASE     )					,						len(SCREAMING_SNAKE_CASE     )     )
																											for jitted_output, output in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
																																		self.assertEqual(jitted_output.shape					,						output.shape     )






						def __UpperCAmelCase	(			self      :List[str]     )	-> Tuple:




													'''simple docstring'''
													_a ,   _a   :			Optional[int]					 =self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__     ):
																											_a   :			str					 =model_class(SCREAMING_SNAKE_CASE     )
																											_a   :			int					 =model.encode(inputs_dict["""input_ids"""]					,						inputs_dict["""attention_mask"""]     )

																											_a   :			List[Any]					 ={
																											    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																											    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																											    """encoder_outputs""": encoder_outputs,
																											}

																											@jax.jit
																											def decode_jitted(SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :List[Any]     ):
																																		return model.decode(
																																		    decoder_input_ids=SCREAMING_SNAKE_CASE					,						decoder_attention_mask=SCREAMING_SNAKE_CASE					,						encoder_outputs=SCREAMING_SNAKE_CASE					,						)

																											with self.subTest("""JIT Enabled"""     ):
																																		_a   :			Any					 =decode_jitted(**SCREAMING_SNAKE_CASE     ).to_tuple()

																											with self.subTest("""JIT Disabled"""     ):
																																		with jax.disable_jit():
																																									_a   :			List[Any]					 =decode_jitted(**SCREAMING_SNAKE_CASE     ).to_tuple()

																											self.assertEqual(len(SCREAMING_SNAKE_CASE     )					,						len(SCREAMING_SNAKE_CASE     )     )
																											for jitted_output, output in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
																																		self.assertEqual(jitted_output.shape					,						output.shape     )






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




													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																				_a   :			List[str]					 =model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""     )
																				# FlaxMarianForSequenceClassification expects eos token in input_ids
																				_a   :			Any					 =np.ones((1, 1)     ) * model.config.eos_token_id
																				_a   :			int					 =model(SCREAMING_SNAKE_CASE     )
																				self.assertIsNotNone(SCREAMING_SNAKE_CASE     )







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						unittest.TestCase				):
						__UpperCamelCase	:  Union[str, Any]     	=							None
						__UpperCamelCase	:  int     	=							None






						@classmethod
						def __UpperCAmelCase	(			cls      :Dict     )	-> None:




													'''simple docstring'''
													_a   :			Optional[int]					 =f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
													return cls






						@cached_property
						def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Union[str, Any]:




													'''simple docstring'''
													return MarianTokenizer.from_pretrained(self.model_name     )






						@property
						def __UpperCAmelCase	(			self      :Optional[int]     )	-> int:




													'''simple docstring'''
													return self.tokenizer.eos_token_id






						@cached_property
						def __UpperCAmelCase	(			self      :List[str]     )	-> int:




													'''simple docstring'''
													_a   :			Union[str, Any]					 =FlaxMarianMTModel.from_pretrained(self.model_name     )
													self.assertEqual(model.config.decoder_start_token_id					,						model.config.pad_token_id     )
													return model






						def __UpperCAmelCase	(			self      :str					,						**SCREAMING_SNAKE_CASE      :Optional[int]     )	-> Optional[int]:




													'''simple docstring'''
													_a   :			Optional[Any]					 =self.translate_src_text(**SCREAMING_SNAKE_CASE     )
													self.assertListEqual(self.expected_text					,						SCREAMING_SNAKE_CASE     )






						def __UpperCAmelCase	(			self      :int					,						**SCREAMING_SNAKE_CASE      :Dict     )	-> Any:




													'''simple docstring'''
													_a   :			str					 =self.tokenizer(self.src_text					,						padding=SCREAMING_SNAKE_CASE					,						return_tensors="""np"""					,						**SCREAMING_SNAKE_CASE     )
													_a   :			List[Any]					 =self.model.generate(
													    model_inputs.input_ids					,						attention_mask=model_inputs.attention_mask					,						num_beams=2					,						max_length=1_2_8					,						).sequences
													_a   :			Optional[int]					 =self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE					,						skip_special_tokens=SCREAMING_SNAKE_CASE     )
													return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  Optional[Any]     	=							"en"
						__UpperCamelCase	:  List[str]     	=							"fr"
						__UpperCamelCase	:  str     	=							[
						    "I am a small frog.",
						    "Now I can forget the 100 words of german that I know.",
						]
						__UpperCamelCase	:  int     	=							[
						    "Je suis une petite grenouille.",
						    "Maintenant, je peux oublier les 100 mots d\'allemand que je connais.",
						]






						@slow
						def __UpperCAmelCase	(			self      :str     )	-> int:




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  int     	=							"fr"
						__UpperCamelCase	:  Union[str, Any]     	=							"en"
						__UpperCamelCase	:  str     	=							[
						    "Donnez moi le micro.",
						    "Tom et Mary étaient assis à une table.",  # Accents
						]
						__UpperCamelCase	:  Any     	=							[
						    "Give me the microphone.",
						    "Tom and Mary were sitting at a table.",
						]






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




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  str     	=							"mt"
						__UpperCamelCase	:  List[str]     	=							"en"
						__UpperCamelCase	:  List[str]     	=							["Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
						__UpperCamelCase	:  Union[str, Any]     	=							["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]






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




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  Any     	=							"en"
						__UpperCamelCase	:  Optional[Any]     	=							"de"
						__UpperCamelCase	:  Optional[Any]     	=							[
						    "I am a small frog.",
						    "Now I can forget the 100 words of german that I know.",
						    "Tom asked his teacher for advice.",
						    "That\'s how I would do it.",
						    "Tom really admired Mary\'s courage.",
						    "Turn around and close your eyes.",
						]
						__UpperCamelCase	:  Tuple     	=							[
						    "Ich bin ein kleiner Frosch.",
						    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
						    "Tom bat seinen Lehrer um Rat.",
						    "So würde ich das machen.",
						    "Tom bewunderte Marias Mut wirklich.",
						    "Drehen Sie sich um und schließen Sie die Augen.",
						]






						@slow
						def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> List[str]:




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  Tuple     	=							"en"
						__UpperCamelCase	:  List[Any]     	=							"zh"
						__UpperCamelCase	:  List[Any]     	=							["My name is Wolfgang and I live in Berlin"]
						__UpperCamelCase	:  Tuple     	=							["我叫沃尔夫冈 我住在柏林"]






						@slow
						def __UpperCAmelCase	(			self      :Optional[int]     )	-> Tuple:




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  Optional[int]     	=							"ru"
						__UpperCamelCase	:  Dict     	=							"fr"
						__UpperCamelCase	:  Tuple     	=							["Он показал мне рукопись своей новой пьесы."]
						__UpperCamelCase	:  Union[str, Any]     	=							["Il m\'a montré le manuscrit de sa nouvelle pièce."]






						@slow
						def __UpperCAmelCase	(			self      :List[str]     )	-> Optional[int]:




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class A__	(						UpperCAmelCase__				):
						__UpperCamelCase	:  List[Any]     	=							"en"
						__UpperCamelCase	:  Any     	=							"ROMANCE"
						__UpperCamelCase	:  str     	=							[
						    ">>fr<< Don\'t spend so much time watching TV.",
						    ">>pt<< Your message has been sent.",
						    ">>es<< He\'s two years older than me.",
						]
						__UpperCamelCase	:  Optional[Any]     	=							[
						    "Ne passez pas autant de temps à regarder la télé.",
						    "A sua mensagem foi enviada.",
						    "Es dos años más viejo que yo.",
						]






						@slow
						def __UpperCAmelCase	(			self      :int     )	-> Union[str, Any]:




													'''simple docstring'''
													self._assert_generated_batch_equal_expected()






'''simple docstring'''







def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]  )  ->      int:
       _a   :			List[str]					 =[701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(_UpperCAmelCase   ,len(_UpperCAmelCase  )  ):
                     _a   :			Tuple					 =collection[i]
                     _a   :			Optional[Any]					 =i
                     while j >= gap and collection[j - gap] > insert_value:
                            _a   :			Union[str, Any]					 =collection[j - gap]
                            j -= gap
                     if j != i:
                            _a   :			Union[str, Any]					 =insert_value
       return collection


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

   testmod()
   A__:   int    =	input('''Enter numbers separated by a comma:\n''').strip()
   A__:   Optional[Any]    =	[int(item) for item in user_input.split(''',''')]
   print(shell_sort(unsorted))


