import heapq




def    lowerCAmelCase_  (		lowercase:				dict		)     ->					set[int]:







 '''simple docstring'''
 _UpperCamelCase:  list[list]    						=			[]

 # for each node and his adjacency list add them and the rank of the node to queue
 # using heapq module the queue will be filled like a Priority Queue
 # heapq works with a min priority queue, so I used -1*len(v) to build it
 for key, value in graph.items():
  # O(log(n))
  heapq.heappush(lowercase	,       [-1 * len(lowercase		), (key, value)]		)

 # chosen_vertices = set of chosen vertices
 _UpperCamelCase:  List[str]    						=			set()

 # while queue isn't empty and there are still edges
 #   (queue[0][0] is the rank of the node with max rank)
 while queue and queue[0][0] != 0:
  # extract vertex with max rank from queue and add it to chosen_vertices
  _UpperCamelCase:  List[Any]    						=			heapq.heappop(lowercase		)[1][0]
  chosen_vertices.add(lowercase		)

  # Remove all arcs adjacent to argmax
  for elem in queue:
   # if v haven't adjacent node, skip
   if elem[0] == 0:
    continue
   # if argmax is reachable from elem
   # remove argmax from elem's adjacent list and update his rank
   if argmax in elem[1][1]:
    _UpperCamelCase:  List[Any]    						=			elem[1][1].index(lowercase		)
    del elem[1][1][index]
    elem[0] += 1
        # re-order the queue
  heapq.heapify(lowercase		)
 return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase_           =				{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
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 			__magic_name__	:





   """simple docstring"""




   lowerCAmelCase   :     Optional[int]	      = LEDConfig
   lowerCAmelCase   :     List[str]	      = {}
   lowerCAmelCase   :     str	      = '''gelu'''



   def __init__(						self       :	int		,	_lowercase       :	Tuple		,	_lowercase       :	Dict=13		,	_lowercase       :	Union[str, Any]=7		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Any=False		,	_lowercase       :	Optional[int]=99		,	_lowercase       :	Dict=32		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Union[str, Any]=4		,	_lowercase       :	List[str]=37		,	_lowercase       :	Optional[Any]=0.1		,	_lowercase       :	List[Any]=0.1		,	_lowercase       :	Union[str, Any]=20		,	_lowercase       :	Tuple=2		,	_lowercase       :	str=1		,	_lowercase       :	Optional[int]=0		,	_lowercase       :	List[str]=4		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			parent
    _UpperCamelCase:  Union[str, Any]    						=			batch_size
    _UpperCamelCase:  Optional[Any]    						=			seq_length
    _UpperCamelCase:  str    						=			is_training
    _UpperCamelCase:  Dict    						=			use_labels
    _UpperCamelCase:  List[Any]    						=			vocab_size
    _UpperCamelCase:  Dict    						=			hidden_size
    _UpperCamelCase:  int    						=			num_hidden_layers
    _UpperCamelCase:  Dict    						=			num_attention_heads
    _UpperCamelCase:  Dict    						=			intermediate_size

    _UpperCamelCase:  Union[str, Any]    						=			hidden_dropout_prob
    _UpperCamelCase:  Any    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Optional[Any]    						=			max_position_embeddings
    _UpperCamelCase:  Union[str, Any]    						=			eos_token_id
    _UpperCamelCase:  Union[str, Any]    						=			pad_token_id
    _UpperCamelCase:  Optional[Any]    						=			bos_token_id
    _UpperCamelCase:  Optional[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
    _UpperCamelCase:  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
    _UpperCamelCase:  Tuple    						=			(
        self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
    )



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size				)
    _UpperCamelCase:  Union[str, Any]    						=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,	1				)
    _UpperCamelCase:  Union[str, Any]    						=			tf.concat([input_ids, eos_tensor]		,	axis=1				)

    _UpperCamelCase:  Union[str, Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Any    						=			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		,	)
    _UpperCamelCase:  Dict    						=			prepare_led_inputs_dict(_lowercase		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			tf.concat(
        [tf.zeros_like(_lowercase				)[:, :-1], tf.ones_like(_lowercase				)[:, -1:]]		,	axis=-1		,	)
    _UpperCamelCase:  List[Any]    						=			global_attention_mask
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[str]		,	_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			TFLEDModel(config=_lowercase				).get_decoder()
    _UpperCamelCase:  Optional[int]    						=			inputs_dict['''input_ids''']

    _UpperCamelCase:  Dict    						=			input_ids[:1, :]
    _UpperCamelCase:  Optional[Any]    						=			inputs_dict['''attention_mask'''][:1, :]
    _UpperCamelCase:  Optional[int]    						=			1

    # first forward pass
    _UpperCamelCase:  Optional[Any]    						=			model(_lowercase		,	attention_mask=_lowercase		,	use_cache=_lowercase				)

    _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			outputs.to_tuple()

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

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

    _UpperCamelCase:  str    						=			model(_lowercase		,	attention_mask=_lowercase				)[0]
    _UpperCamelCase:  Optional[int]    						=			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
    _UpperCamelCase:  Dict    						=			int(ids_tensor((1,)		,	output_from_past.shape[-1]				)				)
    _UpperCamelCase:  Any    						=			output_from_no_past[:, -3:, random_slice_idx]
    _UpperCamelCase:  Optional[Any]    						=			output_from_past[:, :, random_slice_idx]

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




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				List[str]	,       lowercase:				List[str]	,       lowercase:				int=None	,       lowercase:				int=None	,       lowercase:				List[str]=None	,       lowercase:				List[str]=None	,       )     ->					List[str]:







 '''simple docstring'''
 if attention_mask is None:
  _UpperCamelCase:  Optional[Any]    						=			tf.cast(tf.math.not_equal(lowercase	,       config.pad_token_id		)	,       tf.inta		)
 if decoder_attention_mask is None:
  _UpperCamelCase:  int    						=			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:
  _UpperCamelCase:  str    						=			tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  _UpperCamelCase:  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 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[int]	      = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
   lowerCAmelCase   :     List[str]	      = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
   lowerCAmelCase   :     Union[str, Any]	      = (
       {
           '''conversational''': TFLEDForConditionalGeneration,
           '''feature-extraction''': TFLEDModel,
           '''summarization''': TFLEDForConditionalGeneration,
           '''text2text-generation''': TFLEDForConditionalGeneration,
           '''translation''': TFLEDForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase   :     str	      = True
   lowerCAmelCase   :     Any	      = False
   lowerCAmelCase   :     Union[str, Any]	      = False
   lowerCAmelCase   :     Any	      = False



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			TFLEDModelTester(self				)
    _UpperCamelCase:  Any    						=			ConfigTester(self		,	config_class=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



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

    _UpperCamelCase:  int    						=			True
    _UpperCamelCase:  str    						=			self.model_tester.seq_length
    _UpperCamelCase:  Dict    						=			self.model_tester.encoder_seq_length

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

     if self.is_encoder_decoder:
      _UpperCamelCase:  Optional[int]    						=			model_class(_lowercase				)
      _UpperCamelCase:  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"]
     _UpperCamelCase:  Union[str, Any]    						=			True
     _UpperCamelCase:  List[str]    						=			model_class(_lowercase				)
     _UpperCamelCase:  Dict    						=			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
     _UpperCamelCase:  str    						=			True
     _UpperCamelCase:  Dict    						=			True
     _UpperCamelCase:  int    						=			model_class(_lowercase				)
     _UpperCamelCase:  Optional[int]    						=			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 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    pass




def    lowerCAmelCase_  (		lowercase:				Tuple		)     ->					List[str]:







 '''simple docstring'''
 return tf.constant(lowercase	,       dtype=tf.intaa		)


UpperCAmelCase_           =				1E-4





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





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				).led

    # change to intended input here
    _UpperCamelCase:  List[str]    						=			_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  int    						=			_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  Union[str, Any]    						=			prepare_led_inputs_dict(model.config		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			model(**_lowercase				)[0]
    _UpperCamelCase:  Optional[int]    						=			(1, 1_024, 768)
    self.assertEqual(output.shape		,	_lowercase				)
    # change to expected output here
    _UpperCamelCase:  Optional[Any]    						=			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 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				)

    # change to intended input here
    _UpperCamelCase:  Dict    						=			_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  List[str]    						=			_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  Tuple    						=			prepare_led_inputs_dict(model.config		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Any    						=			model(**_lowercase				)[0]
    _UpperCamelCase:  Dict    						=			(1, 1_024, model.config.vocab_size)
    self.assertEqual(output.shape		,	_lowercase				)
    # change to expected output here
    _UpperCamelCase:  Optional[Any]    						=			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				)