


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

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






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

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

           lowerCAmelCase					:	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					:	str       =   prepare_led_inputs_dict(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	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  :					List[Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	Tuple       =   TFLEDModel(config=UpperCamelCase_				).get_decoder()
           lowerCAmelCase					:	Optional[Any]       =   inputs_dict['''input_ids''']

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

           # first forward pass
           lowerCAmelCase					:	List[str]       =   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					:	str       =   ids_tensor((self.batch_size, 3)					,				config.vocab_size				)
           lowerCAmelCase					:	List[Any]       =   tf.cast(ids_tensor((self.batch_size, 3)					,				2				)					,				tf.inta				)

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

           lowerCAmelCase					:	Dict       =   model(UpperCamelCase_					,				attention_mask=UpperCamelCase_				)[0]
           lowerCAmelCase					:	List[Any]       =   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					:	Dict       =   int(ids_tensor((1,)					,				output_from_past.shape[-1]				)				)
           lowerCAmelCase					:	Any       =   output_from_no_past[:, -3:, random_slice_idx]
           lowerCAmelCase					:	str       =   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				:			Tuple ,   _snake_case				:			Tuple ,   _snake_case				:			Optional[Any] ,   _snake_case				:			str=None ,   _snake_case				:			Optional[Any]=None ,   _snake_case				:			List[str]=None ,   _snake_case				:			List[str]=None ,   ):
      if attention_mask is None:
            lowerCAmelCase					:	Any       =   tf.cast(tf.math.not_equal(_snake_case ,   config.pad_token_id			) ,   tf.inta			)
      if decoder_attention_mask is None:
            lowerCAmelCase					:	Union[str, Any]       =   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					:	List[str]       =   tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            lowerCAmelCase					:	Optional[Any]       =   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  :					int				):
           lowerCAmelCase					:	Any       =   TFLEDModelTester(self				)
           lowerCAmelCase					:	List[str]       =   ConfigTester(self					,				config_class=UpperCamelCase_				)






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






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






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

           lowerCAmelCase					:	int       =   True
           lowerCAmelCase					:	str       =   self.model_tester.seq_length
           lowerCAmelCase					:	Tuple       =   self.model_tester.encoder_seq_length

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

                 if self.is_encoder_decoder:
                       lowerCAmelCase					:	List[str]       =   model_class(UpperCamelCase_				)
                       lowerCAmelCase					:	List[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					:	Any       =   True
                 lowerCAmelCase					:	Optional[Any]       =   model_class(UpperCamelCase_				)
                 lowerCAmelCase					:	Tuple       =   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					:	int       =   True
                 lowerCAmelCase					:	int       =   True
                 lowerCAmelCase					:	str       =   model_class(UpperCamelCase_				)
                 lowerCAmelCase					:	Optional[int]       =   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  :					Optional[int]				):
           pass






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           # TODO: Head-masking not yet implement
           pass



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


snake_case__		:     Tuple    				=				1e-4






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






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	int       =   TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				).led

           # change to intended input here
           lowerCAmelCase					:	List[Any]       =   _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					:	Dict       =   _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					:	str       =   prepare_led_inputs_dict(model.config					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	int       =   model(**UpperCamelCase_				)[0]
           lowerCAmelCase					:	List[str]       =   (1, 1_0_2_4, 7_6_8)
           self.assertEqual(output.shape					,				UpperCamelCase_				)
           # change to expected output here
           lowerCAmelCase					:	Optional[Any]       =   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  :					Any				):
           lowerCAmelCase					:	str       =   TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				)

           # change to intended input here
           lowerCAmelCase					:	Union[str, Any]       =   _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					:	Tuple       =   _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					:	Tuple       =   prepare_led_inputs_dict(model.config					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   model(**UpperCamelCase_				)[0]
           lowerCAmelCase					:	List[Any]       =   (1, 1_0_2_4, model.config.vocab_size)
           self.assertEqual(output.shape					,				UpperCamelCase_				)
           # change to expected output here
           lowerCAmelCase					:	Optional[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"""





class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   val
           lowerCAmelCase					:	str       =   None
           lowerCAmelCase					:	Dict       =   None






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           if self.val:
                 if val < self.val:
                       if self.left is None:
                             lowerCAmelCase					:	int       =   Node(UpperCamelCase_				)
                       else:
                             self.left.insert(UpperCamelCase_				)
                 elif val > self.val:
                       if self.right is None:
                             lowerCAmelCase					:	Any       =   Node(UpperCamelCase_				)
                       else:
                             self.right.insert(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   val



def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			str			):
      # Recursive traversal
      if root:
            inorder(root.left ,   _snake_case			)
            res.append(root.val			)
            inorder(root.right ,   _snake_case			)


def      _snake_case						(						_snake_case				:			Optional[Any]			):
      # Build BST
      if len(_snake_case			) == 0:
            return arr
      lowerCAmelCase					:	Optional[Any]       =   Node(arr[0]			)
      for i in range(1 ,   len(_snake_case			)			):
            root.insert(arr[i]			)
      # Traverse BST in order.
      lowerCAmelCase					:	Optional[int]       =   []
      inorder(_snake_case ,   _snake_case			)
      return res


if __name__ == "__main__":
 print(tree_sort([10, 1, 3, 2, 9, 14, 13]))


