





"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Union[str, Any]       =[]
							__lowerCamelCase     ,    __lowerCamelCase   :   int       =input_list[low:mid], input_list[mid : high + 1]
							while left and right:
														result.append((left if left[0] <= right[0] else right).pop(0 ) )
							__lowerCamelCase   :   int       =result + left + right
							return input_list



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list ):


							'''simple docstring'''







							if len(SCREAMING_SNAKE_CASE ) <= 1:
														return input_list
							__lowerCamelCase   :   List[Any]       =list(SCREAMING_SNAKE_CASE )

							# iteration for two-way merging
							__lowerCamelCase   :   List[Any]       =2
							while p <= len(SCREAMING_SNAKE_CASE ):
														# getting low, high and middle value for merge-sort of single list
														for i in range(0     ,    len(SCREAMING_SNAKE_CASE )     ,    SCREAMING_SNAKE_CASE ):
																					__lowerCamelCase   :   List[Any]       =i
																					__lowerCamelCase   :   str       =i + p - 1
																					__lowerCamelCase   :   Dict       =(low + high + 1) // 2
																					__lowerCamelCase   :   Optional[Any]       =merge(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
														# final merge of last two parts
														if p * 2 >= len(SCREAMING_SNAKE_CASE ):
																					__lowerCamelCase   :   Optional[int]       =i
																					__lowerCamelCase   :   Any       =merge(SCREAMING_SNAKE_CASE     ,    0     ,    SCREAMING_SNAKE_CASE     ,    len(SCREAMING_SNAKE_CASE ) - 1 )
																					break
														p *= 2

							return input_list


if __name__ == "__main__":
							_UpperCamelCase					      =   input('Enter numbers separated by a comma:\n').strip()
							if user_input == "":
														_UpperCamelCase					      =   []
							else:
														_UpperCamelCase					      =   [int(item.strip()) for item in user_input.split(',')]
							print(iter_merge_sort(unsorted))











"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							int									=		LEDConfig
				__snake_case :							int									=		{}
				__snake_case :							Any									=		"""gelu"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:List[Any]    ,     __lowercase	:int=13    ,     __lowercase	:Union[str, Any]=7    ,     __lowercase	:List[Any]=True    ,     __lowercase	:List[Any]=False    ,     __lowercase	:str=99    ,     __lowercase	:Optional[int]=32    ,     __lowercase	:List[Any]=2    ,     __lowercase	:str=4    ,     __lowercase	:str=37    ,     __lowercase	:Any=0.1    ,     __lowercase	:List[Any]=0.1    ,     __lowercase	:Optional[Any]=20    ,     __lowercase	:Union[str, Any]=2    ,     __lowercase	:str=1    ,     __lowercase	:List[str]=0    ,     __lowercase	:List[Any]=4    ,     ):
											__lowerCamelCase   :   Union[str, Any]       =parent
											__lowerCamelCase   :   List[Any]       =batch_size
											__lowerCamelCase   :   str       =seq_length
											__lowerCamelCase   :   List[str]       =is_training
											__lowerCamelCase   :   Dict       =use_labels
											__lowerCamelCase   :   int       =vocab_size
											__lowerCamelCase   :   Union[str, Any]       =hidden_size
											__lowerCamelCase   :   Any       =num_hidden_layers
											__lowerCamelCase   :   List[Any]       =num_attention_heads
											__lowerCamelCase   :   str       =intermediate_size

											__lowerCamelCase   :   Optional[Any]       =hidden_dropout_prob
											__lowerCamelCase   :   Optional[Any]       =attention_probs_dropout_prob
											__lowerCamelCase   :   Optional[Any]       =max_position_embeddings
											__lowerCamelCase   :   Optional[Any]       =eos_token_id
											__lowerCamelCase   :   str       =pad_token_id
											__lowerCamelCase   :   Any       =bos_token_id
											__lowerCamelCase   :   Union[str, Any]       =attention_window

											# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
											# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
											# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
											# because its local attention only attends to `self.attention_window` and one before and one after
											__lowerCamelCase   :   List[Any]       =self.attention_window + 2

											# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
											# the `test_attention_outputs` and `test_hidden_states_output` tests
											__lowerCamelCase   :   Optional[Any]       =(
											    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
											)




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Any       =ids_tensor([self.batch_size, self.seq_length - 1]    ,     self.vocab_size      )
											__lowerCamelCase   :   List[str]       =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    ,     1      )
											__lowerCamelCase   :   Tuple       =tf.concat([input_ids, eos_tensor]    ,     axis=1      )

											__lowerCamelCase   :   Optional[int]       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

											__lowerCamelCase   :   Optional[int]       =self.config_cls(
											    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_ids=[2]    ,     bos_token_id=self.bos_token_id    ,     pad_token_id=self.pad_token_id    ,     decoder_start_token_id=self.pad_token_id    ,     attention_window=self.attention_window    ,     **self.config_updates    ,     )
											__lowerCamelCase   :   int       =prepare_led_inputs_dict(__lowercase    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Optional[Any]       =tf.concat(
											    [tf.zeros_like(__lowercase      )[:, :-1], tf.ones_like(__lowercase      )[:, -1:]]    ,     axis=-1    ,     )
											__lowerCamelCase   :   Any       =global_attention_mask
											return config, inputs_dict




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:List[str]    ,     __lowercase	:List[str]      ):
											__lowerCamelCase   :   int       =TFLEDModel(config=__lowercase      ).get_decoder()
											__lowerCamelCase   :   Dict       =inputs_dict['''input_ids''']

											__lowerCamelCase   :   Optional[Any]       =input_ids[:1, :]
											__lowerCamelCase   :   List[Any]       =inputs_dict['''attention_mask'''][:1, :]
											__lowerCamelCase   :   List[Any]       =1

											# first forward pass
											__lowerCamelCase   :   List[Any]       =model(__lowercase    ,     attention_mask=__lowercase    ,     use_cache=__lowercase      )

											__lowerCamelCase     ,    __lowerCamelCase   :   Any       =outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											__lowerCamelCase   :   List[Any]       =ids_tensor((self.batch_size, 3)    ,     config.vocab_size      )
											__lowerCamelCase   :   Union[str, Any]       =tf.cast(ids_tensor((self.batch_size, 3)    ,     2      )    ,     tf.inta      )

											# append to next input_ids and
											__lowerCamelCase   :   Dict       =tf.concat([input_ids, next_tokens]    ,     axis=-1      )
											__lowerCamelCase   :   int       =tf.concat([attention_mask, next_attn_mask]    ,     axis=-1      )

											__lowerCamelCase   :   str       =model(__lowercase    ,     attention_mask=__lowercase      )[0]
											__lowerCamelCase   :   List[str]       =model(__lowercase    ,     attention_mask=__lowercase    ,     past_key_values=__lowercase      )[0]

											self.parent.assertEqual(next_tokens.shape[1]    ,     output_from_past.shape[1]      )

											# select random slice
											__lowerCamelCase   :   List[str]       =int(ids_tensor((1,)    ,     output_from_past.shape[-1]      )      )
											__lowerCamelCase   :   Dict       =output_from_no_past[:, -3:, random_slice_idx]
											__lowerCamelCase   :   int       =output_from_past[:, :, random_slice_idx]

											# test that outputs are equal for slice
											tf.debugging.assert_near(__lowercase    ,     __lowercase    ,     rtol=1e-3      )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				List[Any]     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]=None     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE							:				Tuple=None     ,    ):


							'''simple docstring'''







							if attention_mask is None:
														__lowerCamelCase   :   Optional[int]       =tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE     ,    config.pad_token_id )     ,    tf.inta )
							if decoder_attention_mask is None:
														__lowerCamelCase   :   str       =tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape     ,    dtype=tf.inta ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,    config.pad_token_id )     ,    tf.inta ),
														    ]     ,    axis=-1     ,    )
							if head_mask is None:
														__lowerCamelCase   :   Dict       =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
							if decoder_head_mask is None:
														__lowerCamelCase   :   List[str]       =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							    "decoder_input_ids": decoder_input_ids,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							}
@require_tf
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							str									=		(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				__snake_case :							Tuple									=		(TFLEDForConditionalGeneration,) if is_tf_available() else ()
				__snake_case :							Optional[Any]									=		(
				    {
				        """conversational""": TFLEDForConditionalGeneration,
				        """feature-extraction""": TFLEDModel,
				        """summarization""": TFLEDForConditionalGeneration,
				        """text2text-generation""": TFLEDForConditionalGeneration,
				        """translation""": TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__snake_case :							int									=		True
				__snake_case :							int									=		False
				__snake_case :							Optional[int]									=		False
				__snake_case :							Optional[int]									=		False




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   int       =TFLEDModelTester(self      )
											__lowerCamelCase   :   Any       =ConfigTester(self    ,     config_class=__lowercase      )




				def        __lowercase	(  self	:Dict      ):
											self.config_tester.run_common_tests()




				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   Any       =self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__lowercase      )




				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase   :   int       =tf.zeros_like(inputs_dict['''attention_mask''']      )
											__lowerCamelCase   :   List[Any]       =2
											__lowerCamelCase   :   Optional[Any]       =tf.where(
											    tf.range(self.model_tester.seq_length      )[None, :] < num_global_attn_indices    ,     1    ,     inputs_dict['''global_attention_mask''']    ,     )

											__lowerCamelCase   :   Tuple       =True
											__lowerCamelCase   :   Optional[Any]       =self.model_tester.seq_length
											__lowerCamelCase   :   List[str]       =self.model_tester.encoder_seq_length

											def check_decoder_attentions_output(__lowercase	:Any      ):
																		__lowerCamelCase   :   Union[str, Any]       =outputs.decoder_attentions
																		self.assertEqual(len(__lowercase      )    ,     self.model_tester.num_hidden_layers      )
																		self.assertListEqual(
																		    list(decoder_attentions[0].shape[-3:]      )    ,     [self.model_tester.num_attention_heads, seq_length, seq_length]    ,     )

											def check_encoder_attentions_output(__lowercase	:List[str]      ):
																		__lowerCamelCase   :   Any       =[t.numpy() for t in outputs.encoder_attentions]
																		__lowerCamelCase   :   Optional[int]       =[t.numpy() for t in outputs.encoder_global_attentions]
																		self.assertEqual(len(__lowercase      )    ,     self.model_tester.num_hidden_layers      )
																		self.assertEqual(len(__lowercase      )    ,     self.model_tester.num_hidden_layers      )
																		self.assertListEqual(
																		    list(attentions[0].shape[-3:]      )    ,     [self.model_tester.num_attention_heads, seq_length, seq_length]    ,     )
																		self.assertListEqual(
																		    list(global_attentions[0].shape[-3:]      )    ,     [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    ,     )

											for model_class in self.all_model_classes:
																		__lowerCamelCase   :   List[str]       =True
																		__lowerCamelCase   :   int       =False
																		__lowerCamelCase   :   Optional[Any]       =False
																		__lowerCamelCase   :   str       =model_class(__lowercase      )
																		__lowerCamelCase   :   Any       =model(self._prepare_for_class(__lowercase    ,     __lowercase      )      )
																		__lowerCamelCase   :   int       =len(__lowercase      )
																		self.assertEqual(config.output_hidden_states    ,     __lowercase      )
																		check_encoder_attentions_output(__lowercase      )

																		if self.is_encoder_decoder:
																									__lowerCamelCase   :   Optional[int]       =model_class(__lowercase      )
																									__lowerCamelCase   :   Union[str, Any]       =model(self._prepare_for_class(__lowercase    ,     __lowercase      )      )
																									self.assertEqual(config.output_hidden_states    ,     __lowercase      )
																									check_decoder_attentions_output(__lowercase      )

																		# Check that output attentions can also be changed via the config
																		del inputs_dict["output_attentions"]
																		__lowerCamelCase   :   Any       =True
																		__lowerCamelCase   :   Union[str, Any]       =model_class(__lowercase      )
																		__lowerCamelCase   :   Tuple       =model(self._prepare_for_class(__lowercase    ,     __lowercase      )      )
																		self.assertEqual(config.output_hidden_states    ,     __lowercase      )
																		check_encoder_attentions_output(__lowercase      )

																		# Check attention is always last and order is fine
																		__lowerCamelCase   :   Optional[Any]       =True
																		__lowerCamelCase   :   Optional[int]       =True
																		__lowerCamelCase   :   Dict       =model_class(__lowercase      )
																		__lowerCamelCase   :   Union[str, Any]       =model(self._prepare_for_class(__lowercase    ,     __lowercase      )      )

																		self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    ,     len(__lowercase      )      )
																		self.assertEqual(model.config.output_hidden_states    ,     __lowercase      )
																		check_encoder_attentions_output(__lowercase      )




				@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''      )
				def        __lowercase	(  self	:int      ):
											pass




				def        __lowercase	(  self	:int      ):
											# TODO: Head-masking not yet implement
											pass



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







							return tf.constant(SCREAMING_SNAKE_CASE     ,    dtype=tf.intaa )


_UpperCamelCase					      =   1e-4
@slow
@require_tf
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Dict       =TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''      ).led

											# change to intended input here
											__lowerCamelCase   :   List[str]       =_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Dict       =_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Optional[int]       =prepare_led_inputs_dict(model.config    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       =model(**__lowercase      )[0]
											__lowerCamelCase   :   Optional[int]       =(1, 1024, 768)
											self.assertEqual(output.shape    ,     __lowercase      )
											# change to expected output here
											__lowerCamelCase   :   Dict       =tf.convert_to_tensor(
											    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]    ,     )
											tf.debugging.assert_near(output[:, :3, :3]    ,     __lowercase    ,     atol=1e-3      )




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Any       =TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''      )

											# change to intended input here
											__lowerCamelCase   :   Union[str, Any]       =_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Any       =_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]      )
											__lowerCamelCase   :   Tuple       =prepare_led_inputs_dict(model.config    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   List[Any]       =model(**__lowercase      )[0]
											__lowerCamelCase   :   str       =(1, 1024, model.config.vocab_size)
											self.assertEqual(output.shape    ,     __lowercase      )
											# change to expected output here
											__lowerCamelCase   :   List[str]       =tf.convert_to_tensor(
											    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]    ,     )
											tf.debugging.assert_near(output[:, :3, :3]    ,     __lowercase    ,     atol=1e-3    ,     rtol=1e-3      )




