
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   :
 __lowerCAmelCase  :     List[str]        =			LEDConfig
 __lowerCAmelCase  :     int        =			{}
 __lowerCAmelCase  :     Any        =			'gelu'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=13					,  SCREAMING_SNAKE_CASE_=7					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=99					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=20					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_=4					,  ):


   '''simple docstring'''




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

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


 def      lowercase__     (					self):


   '''simple docstring'''




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

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

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



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      TFLEDModel(config=_A).get_decoder()
   lowercase__  :   Tuple    		=      inputs_dict['input_ids']

   lowercase__  :   Union[str, Any]    		=      input_ids[:1, :]
   lowercase__  :   int    		=      inputs_dict['attention_mask'][:1, :]
   lowercase__  :   Dict    		=      1

   # first forward pass
   lowercase__  :   str    		=      model(_A					,  attention_mask=_A					,  use_cache=_A)

   lowercase__  :   Dict    		=      outputs.to_tuple()

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

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

   lowercase__  :   Optional[int]    		=      model(_A					,  attention_mask=_A)[0]
   lowercase__  :   int    		=      model(_A					,  attention_mask=_A					,  past_key_values=_A)[0]

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

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

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

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , ):


  '''simple docstring'''






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


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   int    		=      TFLEDModelTester(self)
   lowercase__  :   Optional[Any]    		=      ConfigTester(self					,  config_class=_A)


 def      lowercase__     (					self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*_A)


 def      lowercase__     (					self):


   '''simple docstring'''




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

   lowercase__  :   int    		=      True
   lowercase__  :   int    		=      self.model_tester.seq_length
   lowercase__  :   Union[str, Any]    		=      self.model_tester.encoder_seq_length

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

     if self.is_encoder_decoder:
       lowercase__  :   int    		=      model_class(_A)
       lowercase__  :   List[str]    		=      model(self._prepare_for_class(_A					,  _A))
       self.assertEqual(config.output_hidden_states					,  _A)
       check_decoder_attentions_output(_A)

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

     # Check attention is always last and order is fine
     lowercase__  :   Any    		=      True
     lowercase__  :   Union[str, Any]    		=      True
     lowercase__  :   Union[str, Any]    		=      model_class(_A)
     lowercase__  :   Optional[Any]    		=      model(self._prepare_for_class(_A					,  _A))

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


 @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""")
 def      lowercase__     (					self):


   '''simple docstring'''




   pass



 def      lowercase__     (					self):


   '''simple docstring'''




   pass

def 		UpperCamelCase	(      lowercase_   ):


  '''simple docstring'''






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


lowerCamelCase__    :		Union[str, Any]          =   1E-4



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


 def      lowercase__     (					self):


   '''simple docstring'''




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

   # change to intended input here
   lowercase__  :   Dict    		=      _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]])
   lowercase__  :   Dict    		=      _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]])
   lowercase__  :   Union[str, Any]    		=      prepare_led_inputs_dict(model.config					,  _A					,  _A)
   lowercase__  :   Union[str, Any]    		=      model(**_A)[0]
   lowercase__  :   Optional[Any]    		=      (1, 10_24, 7_68)
   self.assertEqual(output.shape					,  _A)
   # change to expected output here
   lowercase__  :   Tuple    		=      tf.convert_to_tensor(
       [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]					,  )
   tf.debugging.assert_near(output[:, :3, :3]					,  _A					,  atol=1E-3)



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""")

   # change to intended input here
   lowercase__  :   List[Any]    		=      _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]])
   lowercase__  :   Any    		=      _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]])
   lowercase__  :   List[str]    		=      prepare_led_inputs_dict(model.config					,  _A					,  _A)
   lowercase__  :   List[str]    		=      model(**_A)[0]
   lowercase__  :   List[Any]    		=      (1, 10_24, model.config.vocab_size)
   self.assertEqual(output.shape					,  _A)
   # change to expected output here
   lowercase__  :   Dict    		=      tf.convert_to_tensor(
       [[33.65_07, 6.4_5_7_2, 16.80_89], [5.8_7_3_9, -2.4_2_3_8, 11.29_02], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]					,  )
   tf.debugging.assert_near(output[:, :3, :3]					,  _A					,  atol=1E-3					,  rtol=1E-3)








from math import factorial

lowerCamelCase__    :		dict[str, int]          =   {str(digit): factorial(digit) for digit in range(1_0)}

def 		UpperCamelCase	(      lowercase_   )		->	int:


  '''simple docstring'''






  if not isinstance(lowercase_ , lowercase_   ):
    raise TypeError("""Parameter number must be int"""   )

  if number < 0:
    raise ValueError("""Parameter number must be greater than or equal to 0"""   )

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(lowercase_   )   )

def 		UpperCamelCase	(      lowercase_ = 60 , lowercase_ = 1_00_00_00   )		->	int:


  '''simple docstring'''






  if not isinstance(lowercase_ , lowercase_   ) or not isinstance(lowercase_ , lowercase_   ):
    raise TypeError("""Parameters chain_length and number_limit must be int"""   )

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        """Parameters chain_length and number_limit must be greater than 0"""   )

  # the counter for the chains with the exact desired length
  lowercase__  :   List[Any]    		=      0
  # the cached sizes of the previous chains
  lowercase__  :   dict[int, int]    		=      {}

  for start_chain_element in range(1 , lowercase_   ):
    # The temporary set will contain the elements of the chain
    lowercase__  :   Optional[Any]    		=      set()
    lowercase__  :   Union[str, Any]    		=      0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    lowercase__  :   Union[str, Any]    		=      start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(lowercase_   )
      chain_set_length += 1
      lowercase__  :   str    		=      digit_factorial_sum(lowercase_   )

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    lowercase__  :   str    		=      chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'''{solution()}''')






