

'''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    a__  :
   _SCREAMING_SNAKE_CASE    :     Union[str, Any]	       =							LEDConfig
   _SCREAMING_SNAKE_CASE    :     Optional[int]	       =							{}
   _SCREAMING_SNAKE_CASE    :     int	       =							"gelu"


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





      """simple docstring"""





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

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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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

      _lowercase			:      List[str]			     =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size  )

      _lowercase			:      Optional[int]			     =   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			:      Optional[int]			     =   prepare_led_inputs_dict(snake_case_			,				snake_case_			,				snake_case_  )
      _lowercase			:      Dict			     =   tf.concat(
          [tf.zeros_like(snake_case_  )[:, :-1], tf.ones_like(snake_case_  )[:, -1:]]			,				axis=-1			,				)
      _lowercase			:      Tuple			     =   global_attention_mask
      return config, inputs_dict






   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   TFLEDModel(config=snake_case_  ).get_decoder()
      _lowercase			:      Dict			     =   inputs_dict['''input_ids''']

      _lowercase			:      Dict			     =   input_ids[:1, :]
      _lowercase			:      Any			     =   inputs_dict['''attention_mask'''][:1, :]
      _lowercase			:      List[str]			     =   1

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

      _lowercase			:      Tuple			     =   outputs.to_tuple()

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

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

      _lowercase			:      Optional[int]			     =   model(snake_case_			,				attention_mask=snake_case_  )[0]
      _lowercase			:      int			     =   model(snake_case_			,				attention_mask=snake_case_			,				past_key_values=snake_case_  )[0]

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

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

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







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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   TFLEDModelTester(self  )
      _lowercase			:      Optional[int]			     =   ConfigTester(self			,				config_class=snake_case_  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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

      _lowercase			:      List[str]			     =   True
      _lowercase			:      Tuple			     =   self.model_tester.seq_length
      _lowercase			:      Any			     =   self.model_tester.encoder_seq_length

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

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

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

         # Check attention is always last and order is fine
         _lowercase			:      List[str]			     =   True
         _lowercase			:      Any			     =   True
         _lowercase			:      Tuple			     =   model_class(snake_case_  )
         _lowercase			:      int			     =   model(self._prepare_for_class(snake_case_			,				snake_case_  )  )

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


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





      """simple docstring"""





      pass






   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      pass







def 					_A		(	snake_case     )			->	Any:
   return tf.constant(lowerCAmelCase__		,							dtype=tf.intaa     )


_snake_case     =  1e-4


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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"  ).led

      # change to intended input here
      _lowercase			:      Union[str, Any]			     =   _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]  )
      _lowercase			:      Dict			     =   _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]  )
      _lowercase			:      List[str]			     =   prepare_led_inputs_dict(model.config			,				snake_case_			,				snake_case_  )
      _lowercase			:      List[str]			     =   model(**snake_case_  )[0]
      _lowercase			:      Any			     =   (1, 1024, 768)
      self.assertEqual(output.shape			,				snake_case_  )
      # change to expected output here
      _lowercase			:      Dict			     =   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]			,				snake_case_			,				atol=1E-3  )






   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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

      # change to intended input here
      _lowercase			:      int			     =   _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]  )
      _lowercase			:      Tuple			     =   _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]  )
      _lowercase			:      Dict			     =   prepare_led_inputs_dict(model.config			,				snake_case_			,				snake_case_  )
      _lowercase			:      List[str]			     =   model(**snake_case_  )[0]
      _lowercase			:      List[Any]			     =   (1, 1024, model.config.vocab_size)
      self.assertEqual(output.shape			,				snake_case_  )
      # change to expected output here
      _lowercase			:      str			     =   tf.convert_to_tensor(
          [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]			,				)
      tf.debugging.assert_near(output[:, :3, :3]			,				snake_case_			,				atol=1E-3			,				rtol=1E-3  )








def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 if n_term == "":
  return []
 __a :  list						=  []
 for temp in range(int(lowerCAmelCase__  )  ):
  series.append(f"1/{temp + 1}" if series else '''1'''  )
 return series


if __name__ == "__main__":
      lowercase__       =input('Enter the last number (nth term) of the Harmonic Series')
      print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
      print(harmonic_series(nth_term))
