


"""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 						snake_case_:
     __UpperCamelCase     =    LEDConfig
     __UpperCamelCase     =    {}
     __UpperCamelCase     =    '''gelu'''






     def __init__(		self     :    Optional[int]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[str]=1_3    , UpperCamelCase_     :    Optional[Any]=7    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    List[str]=False    , UpperCamelCase_     :    List[Any]=9_9    , UpperCamelCase_     :    str=3_2    , UpperCamelCase_     :    Dict=2    , UpperCamelCase_     :    int=4    , UpperCamelCase_     :    Any=3_7    , UpperCamelCase_     :    Optional[Any]=0.1    , UpperCamelCase_     :    int=0.1    , UpperCamelCase_     :    str=2_0    , UpperCamelCase_     :    Any=2    , UpperCamelCase_     :    List[str]=1    , UpperCamelCase_     :    Optional[int]=0    , UpperCamelCase_     :    Any=4    , ):
           lowerCAmelCase    :		List[Any]				=      parent
           lowerCAmelCase    :		Optional[int]				=      batch_size
           lowerCAmelCase    :		int				=      seq_length
           lowerCAmelCase    :		Dict				=      is_training
           lowerCAmelCase    :		int				=      use_labels
           lowerCAmelCase    :		int				=      vocab_size
           lowerCAmelCase    :		List[Any]				=      hidden_size
           lowerCAmelCase    :		Union[str, Any]				=      num_hidden_layers
           lowerCAmelCase    :		Any				=      num_attention_heads
           lowerCAmelCase    :		List[str]				=      intermediate_size

           lowerCAmelCase    :		Any				=      hidden_dropout_prob
           lowerCAmelCase    :		List[Any]				=      attention_probs_dropout_prob
           lowerCAmelCase    :		Dict				=      max_position_embeddings
           lowerCAmelCase    :		List[str]				=      eos_token_id
           lowerCAmelCase    :		Optional[int]				=      pad_token_id
           lowerCAmelCase    :		str				=      bos_token_id
           lowerCAmelCase    :		List[str]				=      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    :		Optional[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    :		Tuple				=      (
               self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
           )






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

           lowerCAmelCase    :		Any				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           lowerCAmelCase    :		List[str]				=      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    :		List[Any]				=      prepare_led_inputs_dict(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase    :		List[Any]				=      tf.concat(
               [tf.zeros_like(UpperCamelCase_ )[:, :-1], tf.ones_like(UpperCamelCase_ )[:, -1:]]    , axis=-1    , )
           lowerCAmelCase    :		Any				=      global_attention_mask
           return config, inputs_dict






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    str    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		str				=      TFLEDModel(config=UpperCamelCase_ ).get_decoder()
           lowerCAmelCase    :		int				=      inputs_dict['''input_ids''']

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

           # first forward pass
           lowerCAmelCase    :		Tuple				=      model(UpperCamelCase_    , attention_mask=UpperCamelCase_    , use_cache=UpperCamelCase_ )

           lowerCAmelCase,					lowerCAmelCase    :		Any				=      outputs.to_tuple()

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

           # append to next input_ids and
           lowerCAmelCase    :		Tuple				=      tf.concat([input_ids, next_tokens]    , axis=-1 )
           lowerCAmelCase    :		Union[str, Any]				=      tf.concat([attention_mask, next_attn_mask]    , axis=-1 )

           lowerCAmelCase    :		str				=      model(UpperCamelCase_    , attention_mask=UpperCamelCase_ )[0]
           lowerCAmelCase    :		Optional[int]				=      model(UpperCamelCase_    , attention_mask=UpperCamelCase_    , past_key_values=UpperCamelCase_ )[0]

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

           # select random slice
           lowerCAmelCase    :		List[Any]				=      int(ids_tensor((1,)    , output_from_past.shape[-1] ) )
           lowerCAmelCase    :		List[Any]				=      output_from_no_past[:, -3:, random_slice_idx]
           lowerCAmelCase    :		Optional[int]				=      output_from_past[:, :, random_slice_idx]

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



def    _snake_case  (  _snake_case :	str       ,      _snake_case :	str       ,      _snake_case :	Union[str, Any]       ,      _snake_case :	str=None       ,      _snake_case :	Optional[Any]=None       ,      _snake_case :	Union[str, Any]=None       ,      _snake_case :	Union[str, Any]=None       ,      ):
      if attention_mask is None:
            lowerCAmelCase    :		Tuple				=      tf.cast(tf.math.not_equal(_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    :		Tuple				=      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
      if decoder_head_mask is None:
            lowerCAmelCase    :		Optional[int]				=      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 						snake_case_(				a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
     __UpperCamelCase     =    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
     __UpperCamelCase     =    (
         {
             '''conversational''': TFLEDForConditionalGeneration,
             '''feature-extraction''': TFLEDModel,
             '''summarization''': TFLEDForConditionalGeneration,
             '''text2text-generation''': TFLEDForConditionalGeneration,
             '''translation''': TFLEDForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __UpperCamelCase     =    True
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		str				=      TFLEDModelTester(self )
           lowerCAmelCase    :		Any				=      ConfigTester(self    , config_class=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           self.config_tester.run_common_tests()






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Tuple				=      self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_ )






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

           lowerCAmelCase    :		int				=      True
           lowerCAmelCase    :		Tuple				=      self.model_tester.seq_length
           lowerCAmelCase    :		Optional[int]				=      self.model_tester.encoder_seq_length

           def check_decoder_attentions_output(UpperCamelCase_     :    List[Any] ):
                 lowerCAmelCase    :		Optional[int]				=      outputs.decoder_attentions
                 self.assertEqual(len(UpperCamelCase_ )    , 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(UpperCamelCase_     :    List[Any] ):
                 lowerCAmelCase    :		Optional[int]				=      [t.numpy() for t in outputs.encoder_attentions]
                 lowerCAmelCase    :		List[str]				=      [t.numpy() for t in outputs.encoder_global_attentions]
                 self.assertEqual(len(UpperCamelCase_ )    , self.model_tester.num_hidden_layers )
                 self.assertEqual(len(UpperCamelCase_ )    , 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[Any]				=      True
                 lowerCAmelCase    :		Union[str, Any]				=      False
                 lowerCAmelCase    :		int				=      False
                 lowerCAmelCase    :		str				=      model_class(UpperCamelCase_ )
                 lowerCAmelCase    :		int				=      model(self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )
                 lowerCAmelCase    :		Union[str, Any]				=      len(UpperCamelCase_ )
                 self.assertEqual(config.output_hidden_states    , UpperCamelCase_ )
                 check_encoder_attentions_output(UpperCamelCase_ )

                 if self.is_encoder_decoder:
                       lowerCAmelCase    :		Optional[Any]				=      model_class(UpperCamelCase_ )
                       lowerCAmelCase    :		Optional[Any]				=      model(self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )
                       self.assertEqual(config.output_hidden_states    , UpperCamelCase_ )
                       check_decoder_attentions_output(UpperCamelCase_ )

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

                 # Check attention is always last and order is fine
                 lowerCAmelCase    :		List[str]				=      True
                 lowerCAmelCase    :		Dict				=      True
                 lowerCAmelCase    :		str				=      model_class(UpperCamelCase_ )
                 lowerCAmelCase    :		Dict				=      model(self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )

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






     @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
     def    lowerCamelCase__   (		self     :    List[Any] ):
           pass






     def    lowerCamelCase__   (		self     :    Tuple ):
           # TODO: Head-masking not yet implement
           pass



def    _snake_case  (  _snake_case :	List[str]	):
      return tf.constant(_snake_case       ,      dtype=tf.intaa	)


snake_case__						:   Union[str, Any]			 = 1e-4






@slow
@require_tf
class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		List[str]				=      TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led

           # change to intended input here
           lowerCAmelCase    :		str				=      _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
           lowerCAmelCase    :		Union[str, Any]				=      _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
           lowerCAmelCase    :		Optional[Any]				=      prepare_led_inputs_dict(model.config    , UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      model(**UpperCamelCase_ )[0]
           lowerCAmelCase    :		List[Any]				=      (1, 1_0_2_4, 7_6_8)
           self.assertEqual(output.shape    , UpperCamelCase_ )
           # change to expected output here
           lowerCAmelCase    :		Optional[int]				=      tf.convert_to_tensor(
               [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]    , )
           tf.debugging.assert_near(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-3 )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )

           # change to intended input here
           lowerCAmelCase    :		Dict				=      _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
           lowerCAmelCase    :		str				=      _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
           lowerCAmelCase    :		Dict				=      prepare_led_inputs_dict(model.config    , UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      model(**UpperCamelCase_ )[0]
           lowerCAmelCase    :		Any				=      (1, 1_0_2_4, model.config.vocab_size)
           self.assertEqual(output.shape    , UpperCamelCase_ )
           # change to expected output here
           lowerCAmelCase    :		Union[str, Any]				=      tf.convert_to_tensor(
               [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]    , )
           tf.debugging.assert_near(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-3    , rtol=1E-3 )






"""simple docstring"""





def    _snake_case  (  _snake_case :	list	):

      def merge(_snake_case :	list       ,      _snake_case :	list	) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0	)
                  yield from left
                  yield from right

            return list(_merge()	)

      if len(_snake_case	) <= 1:
            return collection
      lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	) // 2
      return merge(merge_sort(collection[:mid]	)       ,      merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 snake_case__						:   Optional[Any]			 = input('''Enter numbers separated by a comma:\n''').strip()
 snake_case__						:   Union[str, Any]			 = [int(item) for item in user_input.split(''',''')]
 print(*merge_sort(unsorted), sep=''',''')


