





'''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 							__lowercase    :







	'''simple docstring'''

	a      :			str     =    LEDConfig
	a      :			Any     =    {}
	a      :			Any     =    """gelu"""


	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=13       ,_lowerCamelCase=7       ,_lowerCamelCase=True       ,_lowerCamelCase=False       ,_lowerCamelCase=99       ,_lowerCamelCase=32       ,_lowerCamelCase=2       ,_lowerCamelCase=4       ,_lowerCamelCase=37       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=20       ,_lowerCamelCase=2       ,_lowerCamelCase=1       ,_lowerCamelCase=0       ,_lowerCamelCase=4       ,)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							parent
				__lowercase			  =							batch_size
				__lowercase			  =							seq_length
				__lowercase			  =							is_training
				__lowercase			  =							use_labels
				__lowercase			  =							vocab_size
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_size

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


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







				'''simple docstring'''





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

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

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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							TFLEDModel(config=__a	).get_decoder()
				__lowercase			  =							inputs_dict['''input_ids''']

				__lowercase			  =							input_ids[:1, :]
				__lowercase			  =							inputs_dict['''attention_mask'''][:1, :]
				__lowercase			  =							1

				# first forward pass
				__lowercase			  =							model(__a       ,attention_mask=__a       ,use_cache=__a	)

				__lowercase  ,						__lowercase			  =							outputs.to_tuple()

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

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

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

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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Dict						,    lowerCamelCase_     :					Union[str, Any]=None						,    lowerCamelCase_     :					Dict=None						,    lowerCamelCase_     :					List[str]=None						,    lowerCamelCase_     :					Tuple=None						,    ):




			if attention_mask is None:
						__lowercase			  =							tf.cast(tf.math.not_equal(snake_case_						,    config.pad_token_id			)						,    tf.inta			)
			if decoder_attention_mask is None:
						__lowercase			  =							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			  =							tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						__lowercase			  =							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 							__lowercase    ( UpperCamelCase__	, UpperCamelCase__	, unittest.TestCase  ):







	'''simple docstring'''

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


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							TFLEDModelTester(self	)
				__lowercase			  =							ConfigTester(self       ,config_class=__a	)


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				self.config_tester.run_common_tests()


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*__a	)


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





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

				__lowercase			  =							True
				__lowercase			  =							self.model_tester.seq_length
				__lowercase			  =							self.model_tester.encoder_seq_length

				def check_decoder_attentions_output(_lowerCamelCase	):
							__lowercase			  =							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(_lowerCamelCase	):
							__lowercase			  =							[t.numpy() for t in outputs.encoder_attentions]
							__lowercase			  =							[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			  =							True
							__lowercase			  =							False
							__lowercase			  =							False
							__lowercase			  =							model_class(__a	)
							__lowercase			  =							model(self._prepare_for_class(__a       ,__a	)	)
							__lowercase			  =							len(__a	)
							self.assertEqual(config.output_hidden_states       ,__a	)
							check_encoder_attentions_output(__a	)

							if self.is_encoder_decoder:
										__lowercase			  =							model_class(__a	)
										__lowercase			  =							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			  =							True
							__lowercase			  =							model_class(__a	)
							__lowercase			  =							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			  =							True
							__lowercase			  =							True
							__lowercase			  =							model_class(__a	)
							__lowercase			  =							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   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				pass


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				pass





def 				_lowerCAmelCase  (			lowerCamelCase_     :					Dict			):
			return tf.constant(snake_case_						,    dtype=tf.intaa			)


_SCREAMING_SNAKE_CASE   =      1e-4






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







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''	).led

				# change to intended input here
				__lowercase			  =							_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
				__lowercase			  =							_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
				__lowercase			  =							prepare_led_inputs_dict(model.config       ,__a       ,__a	)
				__lowercase			  =							model(**__a	)[0]
				__lowercase			  =							(1, 1024, 768)
				self.assertEqual(output.shape       ,__a	)
				# change to expected output here
				__lowercase			  =							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   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''	)

				# change to intended input here
				__lowercase			  =							_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
				__lowercase			  =							_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]	)
				__lowercase			  =							prepare_led_inputs_dict(model.config       ,__a       ,__a	)
				__lowercase			  =							model(**__a	)[0]
				__lowercase			  =							(1, 1024, model.config.vocab_size)
				self.assertEqual(output.shape       ,__a	)
				# change to expected output here
				__lowercase			  =							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]       ,__a       ,atol=1E-3       ,rtol=1E-3	)












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						__lowercase			  =							0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors




def 				_lowerCAmelCase  (			):
			__lowercase			  =							1
			__lowercase			  =							1

			while True:
						i += 1
						t_num += i

						if count_divisors(lowerCamelCase_			) > 5_0_0:
									break

			return t_num


if __name__ == "__main__":
	print(solution())





