




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
						SCREAMING_SNAKE_CASE:    List[str]											=   '''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 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	)->  List[Any]:
		if attention_mask is None:
				SCREAMING_SNAKE_CASE_							=       np.where(input_ids != config.pad_token_id						,	1						,	0   )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_							=       np.where(decoder_input_ids != config.pad_token_id						,	1						,	0   )
		if head_mask is None:
				SCREAMING_SNAKE_CASE_							=       np.ones((config.encoder_layers, config.encoder_attention_heads)   )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_							=       np.ones((config.decoder_layers, config.decoder_attention_heads)   )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_							=       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       lowercase_		:



						def __init__( self  :      Dict				,       snake_case__  :      Optional[int]				,       snake_case__  :      Tuple=13				,       snake_case__  :      Union[str, Any]=7				,       snake_case__  :      Union[str, Any]=True				,       snake_case__  :      Union[str, Any]=False				,       snake_case__  :      Optional[int]=99				,       snake_case__  :      Any=16				,       snake_case__  :      Optional[int]=2				,       snake_case__  :      int=4				,       snake_case__  :      int=4				,       snake_case__  :      int="gelu"				,       snake_case__  :      Optional[int]=0.1				,       snake_case__  :      Optional[int]=0.1				,       snake_case__  :      Optional[int]=32				,       snake_case__  :      List[str]=2				,       snake_case__  :      Any=1				,       snake_case__  :      Union[str, Any]=0				,       snake_case__  :      Optional[int]=0.02				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       parent
								SCREAMING_SNAKE_CASE_							=       batch_size
								SCREAMING_SNAKE_CASE_							=       seq_length
								SCREAMING_SNAKE_CASE_							=       is_training
								SCREAMING_SNAKE_CASE_							=       use_labels
								SCREAMING_SNAKE_CASE_							=       vocab_size
								SCREAMING_SNAKE_CASE_							=       hidden_size
								SCREAMING_SNAKE_CASE_							=       num_hidden_layers
								SCREAMING_SNAKE_CASE_							=       num_attention_heads
								SCREAMING_SNAKE_CASE_							=       intermediate_size
								SCREAMING_SNAKE_CASE_							=       hidden_act
								SCREAMING_SNAKE_CASE_							=       hidden_dropout_prob
								SCREAMING_SNAKE_CASE_							=       attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_							=       max_position_embeddings
								SCREAMING_SNAKE_CASE_							=       eos_token_id
								SCREAMING_SNAKE_CASE_							=       pad_token_id
								SCREAMING_SNAKE_CASE_							=       bos_token_id
								SCREAMING_SNAKE_CASE_							=       initializer_range



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,       self.vocab_size     )				,       3				,       self.vocab_size     )
								SCREAMING_SNAKE_CASE_							=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,       dtype=np.intaa     ))				,       -1     )

								SCREAMING_SNAKE_CASE_							=       shift_tokens_right(snake_case__				,       1				,       2     )

								SCREAMING_SNAKE_CASE_							=       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=snake_case__				,       )
								SCREAMING_SNAKE_CASE_							=       prepare_marian_inputs_dict(snake_case__				,       snake_case__				,       snake_case__     )
								return config, inputs_dict



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.prepare_config_and_inputs()
								return config, inputs_dict



						def   __a				( self  :      List[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      Optional[Any]				,       snake_case__  :      int     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       20
								SCREAMING_SNAKE_CASE_							=       model_class_name(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       model.encode(inputs_dict['input_ids']     )

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								SCREAMING_SNAKE_CASE_							=       model.init_cache(decoder_input_ids.shape[0]				,       snake_case__				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,       dtype='i4'     )

								SCREAMING_SNAKE_CASE_							=       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )
								SCREAMING_SNAKE_CASE_							=       model.decode(
								    decoder_input_ids[:, :-1]				,       snake_case__				,       decoder_attention_mask=snake_case__				,       past_key_values=snake_case__				,       decoder_position_ids=snake_case__				,       )

								SCREAMING_SNAKE_CASE_							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='i4'     )
								SCREAMING_SNAKE_CASE_							=       model.decode(
								    decoder_input_ids[:, -1:]				,       snake_case__				,       decoder_attention_mask=snake_case__				,       past_key_values=outputs_cache.past_key_values				,       decoder_position_ids=snake_case__				,       )

								SCREAMING_SNAKE_CASE_							=       model.decode(snake_case__				,       snake_case__     )

								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      str				,       snake_case__  :      List[Any]				,       snake_case__  :      Optional[int]				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       20
								SCREAMING_SNAKE_CASE_							=       model_class_name(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       model.encode(inputs_dict['input_ids']     )

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								SCREAMING_SNAKE_CASE_							=       jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
								    ]				,       axis=-1				,       )

								SCREAMING_SNAKE_CASE_							=       model.init_cache(decoder_input_ids.shape[0]				,       snake_case__				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )

								SCREAMING_SNAKE_CASE_							=       model.decode(
								    decoder_input_ids[:, :-1]				,       snake_case__				,       decoder_attention_mask=snake_case__				,       past_key_values=snake_case__				,       decoder_position_ids=snake_case__				,       )
								SCREAMING_SNAKE_CASE_							=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='i4'     )
								SCREAMING_SNAKE_CASE_							=       model.decode(
								    decoder_input_ids[:, -1:]				,       snake_case__				,       past_key_values=outputs_cache.past_key_values				,       decoder_attention_mask=snake_case__				,       decoder_position_ids=snake_case__				,       )

								SCREAMING_SNAKE_CASE_							=       model.decode(snake_case__				,       snake_case__				,       decoder_attention_mask=snake_case__     )

								SCREAMING_SNAKE_CASE_							=       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       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase       ,  SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=True
						lowerCAmelCase__ 			=(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						lowerCAmelCase__ 			=(FlaxMarianMTModel,) if is_flax_available() else ()



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       FlaxMarianModelTester(self     )



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(snake_case__				,       snake_case__				,       snake_case__     )



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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(snake_case__				,       snake_case__				,       snake_case__     )



						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__     ):
												SCREAMING_SNAKE_CASE_							=       self._prepare_for_class(snake_case__				,       snake_case__     )
												SCREAMING_SNAKE_CASE_							=       model_class(snake_case__     )

												@jax.jit
												def encode_jitted(snake_case__  :      Tuple				,       snake_case__  :      Dict=None				,       **snake_case__  :      Union[str, Any]     ):
														return model.encode(input_ids=snake_case__				,       attention_mask=snake_case__     )

												with self.subTest('JIT Enabled'     ):
														SCREAMING_SNAKE_CASE_							=       encode_jitted(**snake_case__     ).to_tuple()

												with self.subTest('JIT Disabled'     ):
														with jax.disable_jit():
																SCREAMING_SNAKE_CASE_							=       encode_jitted(**snake_case__     ).to_tuple()

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



						def   __a				( self  :      int     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__     ):
												SCREAMING_SNAKE_CASE_							=       model_class(snake_case__     )
												SCREAMING_SNAKE_CASE_							=       model.encode(inputs_dict['input_ids']				,       inputs_dict['attention_mask']     )

												SCREAMING_SNAKE_CASE_							=       {
												    '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(snake_case__  :      Optional[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      Optional[Any]     ):
														return model.decode(
														    decoder_input_ids=snake_case__				,       decoder_attention_mask=snake_case__				,       encoder_outputs=snake_case__				,       )

												with self.subTest('JIT Enabled'     ):
														SCREAMING_SNAKE_CASE_							=       decode_jitted(**snake_case__     ).to_tuple()

												with self.subTest('JIT Disabled'     ):
														with jax.disable_jit():
																SCREAMING_SNAKE_CASE_							=       decode_jitted(**snake_case__     ).to_tuple()

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






						@slow
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								for model_class_name in self.all_model_classes:
										SCREAMING_SNAKE_CASE_							=       model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'     )
										# FlaxMarianForSequenceClassification expects eos token in input_ids
										SCREAMING_SNAKE_CASE_							=       np.ones((1, 1)     ) * model.config.eos_token_id
										SCREAMING_SNAKE_CASE_							=       model(snake_case__     )
										self.assertIsNotNone(snake_case__     )



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(unittest.TestCase   ):
						lowerCAmelCase__ 			=None
						lowerCAmelCase__ 			=None



						@classmethod
						def   __a				( cls  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
								return cls



						@cached_property
						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								return MarianTokenizer.from_pretrained(self.model_name     )



						@property
						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								return self.tokenizer.eos_token_id



						@cached_property
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       FlaxMarianMTModel.from_pretrained(self.model_name     )
								self.assertEqual(model.config.decoder_start_token_id				,       model.config.pad_token_id     )
								return model



						def   __a				( self  :      List[Any]				,       **snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.translate_src_text(**snake_case__     )
								self.assertListEqual(self.expected_text				,       snake_case__     )






						def   __a				( self  :      Optional[int]				,       **snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tokenizer(self.src_text				,       padding=snake_case__				,       return_tensors='np'				,       **snake_case__     )
								SCREAMING_SNAKE_CASE_							=       self.model.generate(
								    model_inputs.input_ids				,       attention_mask=model_inputs.attention_mask				,       num_beams=2				,       max_length=1_28				,       ).sequences
								SCREAMING_SNAKE_CASE_							=       self.tokenizer.batch_decode(snake_case__				,       skip_special_tokens=snake_case__     )
								return generated_words



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="en"
						lowerCAmelCase__ 			="fr"
						lowerCAmelCase__ 			=[
						    "I am a small frog.",
						    "Now I can forget the 100 words of german that I know.",
						]
						lowerCAmelCase__ 			=[
						    "Je suis une petite grenouille.",
						    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
						]



						@slow
						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="fr"
						lowerCAmelCase__ 			="en"
						lowerCAmelCase__ 			=[
						    "Donnez moi le micro.",
						    "Tom et Mary étaient assis à une table.",  # Accents
						]
						lowerCAmelCase__ 			=[
						    "Give me the microphone.",
						    "Tom and Mary were sitting at a table.",
						]



						@slow
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()



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



						@slow
						def   __a				( self  :      str     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="en"
						lowerCAmelCase__ 			="de"
						lowerCAmelCase__ 			=[
						    "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.",
						]
						lowerCAmelCase__ 			=[
						    "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   __a				( self  :      Tuple     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="en"
						lowerCAmelCase__ 			="zh"
						lowerCAmelCase__ 			=["My name is Wolfgang and I live in Berlin"]
						lowerCAmelCase__ 			=["我叫沃尔夫冈 我住在柏林"]



						@slow
						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="ru"
						lowerCAmelCase__ 			="fr"
						lowerCAmelCase__ 			=["Он показал мне рукопись своей новой пьесы."]
						lowerCAmelCase__ 			=["Il m'a montré le manuscrit de sa nouvelle pièce."]



						@slow
						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()



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



						@slow
						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								self._assert_generated_batch_equal_expected()





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[str]											=   input('''Enter numbers separated by a comma:\n''').strip()
						SCREAMING_SNAKE_CASE:    Any											=   [int(item) for item in user_input.split(''',''')]
						print(merge_insertion_sort(unsorted))