





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 				__lowerCAmelCase       :
      _lowercase				:					Optional[int]						=			LEDConfig
      _lowercase				:					Optional[Any]						=			{}
      _lowercase				:					Optional[int]						=			"""gelu"""







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=1_3		,      lowerCAmelCase__=7		,      lowerCAmelCase__=True		,      lowerCAmelCase__=False		,      lowerCAmelCase__=9_9		,      lowerCAmelCase__=3_2		,      lowerCAmelCase__=2		,      lowerCAmelCase__=4		,      lowerCAmelCase__=3_7		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=2_0		,      lowerCAmelCase__=2		,      lowerCAmelCase__=1		,      lowerCAmelCase__=0		,      lowerCAmelCase__=4		,      )   ->						int:



        '''simple docstring'''




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

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







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




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

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

        a__	:					Tuple										=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		,      )
        a__	:					int										=prepare_led_inputs_dict(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					str										=tf.concat(
            [tf.zeros_like(lowerCAmelCase__							)[:, :-1], tf.ones_like(lowerCAmelCase__							)[:, -1:]]		,      axis=-1		,      )
        a__	:					Dict										=global_attention_mask
        return config, inputs_dict







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Any										=TFLEDModel(config=lowerCAmelCase__							).get_decoder()
        a__	:					Tuple										=inputs_dict["input_ids"]

        a__	:					Any										=input_ids[:1, :]
        a__	:					List[str]										=inputs_dict["attention_mask"][:1, :]
        a__	:					List[Any]										=1

        # first forward pass
        a__	:					Any										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__		,      use_cache=lowerCAmelCase__							)

        a__					,							a__	:					Optional[int]										=outputs.to_tuple()

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

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

        a__	:					List[Any]										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					Tuple										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__		,      past_key_values=lowerCAmelCase__							)[0]

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

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

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowerCAmelCase__		,      lowerCAmelCase__		,      rtol=1E-3							)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   str=None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       ):





  """simple docstring"""



  if attention_mask is None:
    a__	:					Optional[int]										=tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE       ,       config.pad_token_id						)       ,       tf.inta						)
  if decoder_attention_mask is None:
    a__	:					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:
    a__	:					int										=tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
  if decoder_head_mask is None:
    a__	:					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 				__lowerCAmelCase       (    UpperCamelCase__						,				UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      _lowercase				:					Union[str, Any]						=			(TFLEDForConditionalGeneration,) if is_tf_available() else ()
      _lowercase				:					Optional[int]						=			(
          {
              """conversational""": TFLEDForConditionalGeneration,
              """feature-extraction""": TFLEDModel,
              """summarization""": TFLEDForConditionalGeneration,
              """text2text-generation""": TFLEDForConditionalGeneration,
              """translation""": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _lowercase				:					Union[str, Any]						=			True
      _lowercase				:					List[str]						=			False
      _lowercase				:					List[str]						=			False
      _lowercase				:					Dict						=			False







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					List[str]										=TFLEDModelTester(self							)
        a__	:					Any										=ConfigTester(self		,      config_class=lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Any										=self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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

        a__	:					Dict										=True
        a__	:					List[Any]										=self.model_tester.seq_length
        a__	:					Dict										=self.model_tester.encoder_seq_length

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

          if self.is_encoder_decoder:
            a__	:					Union[str, Any]										=model_class(lowerCAmelCase__							)
            a__	:					Dict										=model(self._prepare_for_class(lowerCAmelCase__		,      lowerCAmelCase__							)							)
            self.assertEqual(config.output_hidden_states		,      lowerCAmelCase__							)
            check_decoder_attentions_output(lowerCAmelCase__							)

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

          # Check attention is always last and order is fine
          a__	:					int										=True
          a__	:					Tuple										=True
          a__	:					Tuple										=model_class(lowerCAmelCase__							)
          a__	:					Optional[Any]										=model(self._prepare_for_class(lowerCAmelCase__		,      lowerCAmelCase__							)							)

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







      @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."							)
      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        pass
def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



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


UpperCAmelCase	:	Dict		     =       1E-4







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







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Tuple										=TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"							).led

        # change to intended input here
        a__	:					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]]							)
        a__	:					int										=_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]]							)
        a__	:					List[str]										=prepare_led_inputs_dict(model.config		,      lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					Tuple										=model(**lowerCAmelCase__							)[0]
        a__	:					Optional[int]										=(1, 1_0_2_4, 7_6_8)
        self.assertEqual(output.shape		,      lowerCAmelCase__							)
        # change to expected output here
        a__	:					Optional[int]										=tf.convert_to_tensor(
            [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]		,      )
        tf.debugging.assert_near(output[:, :3, :3]		,      lowerCAmelCase__		,      atol=1E-3							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Tuple										=TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"							)

        # change to intended input here
        a__	:					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]]							)
        a__	:					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]]							)
        a__	:					int										=prepare_led_inputs_dict(model.config		,      lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					Optional[int]										=model(**lowerCAmelCase__							)[0]
        a__	:					Any										=(1, 1_0_2_4, model.config.vocab_size)
        self.assertEqual(output.shape		,      lowerCAmelCase__							)
        # change to expected output here
        a__	:					Dict										=tf.convert_to_tensor(
            [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]		,      )
        tf.debugging.assert_near(output[:, :3, :3]		,      lowerCAmelCase__		,      atol=1E-3		,      rtol=1E-3							)













def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]						):





  """simple docstring"""



  a__	:					Optional[Any]										=len(SCREAMING_SNAKE_CASE						)
  while cur > 1:
    # Find the maximum number in arr
    a__	:					List[Any]										=arr.index(max(arr[0:cur]						)						)
    # Reverse from 0 to mi
    a__	:					int										=arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE						)]
    # Reverse whole list
    a__	:					List[str]										=arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE						)]
    cur -= 1
  return arr


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






