






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'''







  _lowerCamelCase    =  LEDConfig
  _lowerCamelCase    =  {}
  _lowerCamelCase    =  '''gelu'''


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


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    seq_length
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    vocab_size
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size

         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    max_position_embeddings
         _UpperCAmelCase							=    eos_token_id
         _UpperCAmelCase							=    pad_token_id
         _UpperCAmelCase							=    bos_token_id
         _UpperCAmelCase							=    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							=    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							=    (
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length - 1]       ,				self.vocab_size							)
         _UpperCAmelCase							=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)       ,				1							)
         _UpperCAmelCase							=    tf.concat([input_ids, eos_tensor]       ,				axis=1							)

         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size							)

         _UpperCAmelCase							=    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							=    prepare_led_inputs_dict(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    tf.concat(
             [tf.zeros_like(lowerCamelCase							)[:, :-1], tf.ones_like(lowerCamelCase							)[:, -1:]]       ,				axis=-1       ,				)
         _UpperCAmelCase							=    global_attention_mask
         return config, inputs_dict





  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    TFLEDModel(config=lowerCamelCase							).get_decoder()
         _UpperCAmelCase							=    inputs_dict["""input_ids"""]

         _UpperCAmelCase							=    input_ids[:1, :]
         _UpperCAmelCase							=    inputs_dict["""attention_mask"""][:1, :]
         _UpperCAmelCase							=    1

         # first forward pass
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				use_cache=lowerCamelCase							)

         _UpperCAmelCase	,			_UpperCAmelCase							=    outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         _UpperCAmelCase							=    ids_tensor((self.batch_size, 3)       ,				config.vocab_size							)
         _UpperCAmelCase							=    tf.cast(ids_tensor((self.batch_size, 3)       ,				2							)       ,				tf.inta							)

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

         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase							)[0]
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    int(ids_tensor((1,)       ,				output_from_past.shape[-1]							)							)
         _UpperCAmelCase							=    output_from_no_past[:, -3:, random_slice_idx]
         _UpperCAmelCase							=    output_from_past[:, :, random_slice_idx]

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




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None   ,      __snake_case=None   ,      __snake_case=None   ,      __snake_case=None   ,      )				->						List[Any]:
       if attention_mask is None:
              _UpperCAmelCase							=    tf.cast(tf.math.not_equal(__snake_case   ,      config.pad_token_id )   ,      tf.inta )
       if decoder_attention_mask is None:
              _UpperCAmelCase							=    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							=    tf.ones((config.encoder_layers, config.encoder_attention_heads) )
       if decoder_head_mask is None:
              _UpperCAmelCase							=    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__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  _lowerCamelCase    =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
  _lowerCamelCase    =  (
      {
          '''conversational''': TFLEDForConditionalGeneration,
          '''feature-extraction''': TFLEDModel,
          '''summarization''': TFLEDForConditionalGeneration,
          '''text2text-generation''': TFLEDForConditionalGeneration,
          '''translation''': TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    TFLEDModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    tf.zeros_like(inputs_dict["""attention_mask"""]							)
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    tf.where(
             tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices       ,				1       ,				inputs_dict["""global_attention_mask"""]       ,				)

         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    self.model_tester.seq_length
         _UpperCAmelCase							=    self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(lowerCamelCase		:			Optional[Any]							):
                _UpperCAmelCase							=    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		:			Any							):
                _UpperCAmelCase							=    [t.numpy() for t in outputs.encoder_attentions]
                _UpperCAmelCase							=    [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:
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    False
                _UpperCAmelCase							=    False
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                _UpperCAmelCase							=    model(self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)
                _UpperCAmelCase							=    len(lowerCamelCase							)
                self.assertEqual(config.output_hidden_states       ,				lowerCamelCase							)
                check_encoder_attentions_output(lowerCamelCase							)

                if self.is_encoder_decoder:
                       _UpperCAmelCase							=    model_class(lowerCamelCase							)
                       _UpperCAmelCase							=    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"]
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                _UpperCAmelCase							=    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
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]							)						->	Tuple:


         """simple docstring"""
         pass





  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         # TODO: Head-masking not yet implement
         pass




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Any:
       return tf.constant(__snake_case   ,      dtype=tf.intaa )


__a:     int   					=					1E-4





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

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							).led

         # change to intended input here
         _UpperCAmelCase							=    _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
         _UpperCAmelCase							=    _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
         _UpperCAmelCase							=    prepare_led_inputs_dict(model.config       ,				lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    model(**lowerCamelCase							)[0]
         _UpperCAmelCase							=    (1, 1024, 768)
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # change to expected output here
         _UpperCAmelCase							=    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]       ,				lowerCamelCase       ,				atol=1E-3							)





  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							)

         # change to intended input here
         _UpperCAmelCase							=    _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
         _UpperCAmelCase							=    _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
         _UpperCAmelCase							=    prepare_led_inputs_dict(model.config       ,				lowerCamelCase       ,				lowerCamelCase							)
         _UpperCAmelCase							=    model(**lowerCamelCase							)[0]
         _UpperCAmelCase							=    (1, 1024, model.config.vocab_size)
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # change to expected output here
         _UpperCAmelCase							=    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]       ,				lowerCamelCase       ,				atol=1E-3       ,				rtol=1E-3							)




"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



