


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 	UpperCAmelCase_					:

   '''simple docstring'''




   __A :      int     		=		LEDConfig
   __A :      Tuple     		=		{}
   __A :      List[Any]     		=		"gelu"






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






     """simple docstring"""





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

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






   def _snake_case    (		self ):






     """simple docstring"""





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

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

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






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				TFLEDModel(config=UpperCAmelCase__ ).get_decoder()
     lowerCamelCase :					int    =				inputs_dict["input_ids"]

     lowerCamelCase :					Union[str, Any]    =				input_ids[:1, :]
     lowerCamelCase :					int    =				inputs_dict["attention_mask"][:1, :]
     lowerCamelCase :					Dict    =				1

     # first forward pass
     lowerCamelCase :					List[str]    =				model(UpperCAmelCase__						,				attention_mask=UpperCAmelCase__						,				use_cache=UpperCAmelCase__ )

     lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				outputs.to_tuple()

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

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

     lowerCamelCase :					List[str]    =				model(UpperCAmelCase__						,				attention_mask=UpperCAmelCase__ )[0]
     lowerCamelCase :					Optional[int]    =				model(UpperCAmelCase__						,				attention_mask=UpperCAmelCase__						,				past_key_values=UpperCAmelCase__ )[0]

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

     # select random slice
     lowerCamelCase :					Union[str, Any]    =				int(ids_tensor((1,)						,				output_from_past.shape[-1] ) )
     lowerCamelCase :					List[str]    =				output_from_no_past[:, -3:, random_slice_idx]
     lowerCamelCase :					Tuple    =				output_from_past[:, :, random_slice_idx]

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






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


  '''simple docstring'''

  if attention_mask is None:
    lowerCamelCase :					Optional[Any]    =				tf.cast(tf.math.not_equal(lowerCAmelCase_      ,						config.pad_token_id	)      ,						tf.inta	)
  if decoder_attention_mask is None:
    lowerCamelCase :					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:
    lowerCamelCase :					str    =				tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    lowerCamelCase :					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 	UpperCAmelCase_					(					UpperCamelCase		, UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				TFLEDModelTester(self )
     lowerCamelCase :					Dict    =				ConfigTester(self						,				config_class=UpperCAmelCase__ )






   def _snake_case    (		self ):






     """simple docstring"""





     self.config_tester.run_common_tests()






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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

     lowerCamelCase :					int    =				True
     lowerCamelCase :					List[str]    =				self.model_tester.seq_length
     lowerCamelCase :					Optional[Any]    =				self.model_tester.encoder_seq_length

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

       if self.is_encoder_decoder:
         lowerCamelCase :					List[str]    =				model_class(UpperCAmelCase__ )
         lowerCamelCase :					Tuple    =				model(self._prepare_for_class(UpperCAmelCase__						,				UpperCAmelCase__ ) )
         self.assertEqual(config.output_hidden_states						,				UpperCAmelCase__ )
         check_decoder_attentions_output(UpperCAmelCase__ )

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

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

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






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






     """simple docstring"""





     pass






   def _snake_case    (		self ):






     """simple docstring"""





     pass






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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


_snake_case             =     1E-4







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

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





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

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )

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







"""simple docstring"""







import math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
