"""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        snake_case :




					"""simple docstring"""

					snake_case__       					=				LEDConfig
					snake_case__       					=				{}
					snake_case__       					=				"gelu"







					def __init__(       self     :			int					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Tuple=13					,lowerCamelCase__     :			int=7					,lowerCamelCase__     :			List[Any]=True					,lowerCamelCase__     :			Optional[Any]=False					,lowerCamelCase__     :			Union[str, Any]=99					,lowerCamelCase__     :			str=32					,lowerCamelCase__     :			List[str]=2					,lowerCamelCase__     :			Optional[int]=4					,lowerCamelCase__     :			List[Any]=37					,lowerCamelCase__     :			Optional[int]=0.1					,lowerCamelCase__     :			Dict=0.1					,lowerCamelCase__     :			Optional[Any]=20					,lowerCamelCase__     :			Union[str, Any]=2					,lowerCamelCase__     :			Any=1					,lowerCamelCase__     :			int=0					,lowerCamelCase__     :			List[Any]=4					,):
									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     :			int    ):
									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(_A					,_A					,_A    )
									UpperCAmelCase__    =							tf.concat(
									    [tf.zeros_like(_A    )[:, :-1], tf.ones_like(_A    )[:, -1:]]					,axis=-1					,)
									UpperCAmelCase__    =							global_attention_mask
									return config, inputs_dict







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__    =							TFLEDModel(config=_A    ).get_decoder()
									UpperCAmelCase__    =							inputs_dict['input_ids']

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

									# first forward pass
									UpperCAmelCase__    =							model(_A					,attention_mask=_A					,use_cache=_A    )

									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(_A					,attention_mask=_A    )[0]
									UpperCAmelCase__    =							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
									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(_A					,_A					,rtol=1e-3    )






def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    ):



				if attention_mask is None:
								UpperCAmelCase__    =							tf.cast(tf.math.not_equal(_lowerCamelCase      ,    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        snake_case (	a__   ,					a__   ,					unittest.TestCase       ):




					"""simple docstring"""

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







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							TFLEDModelTester(self    )
									UpperCAmelCase__    =							ConfigTester(self					,config_class=_A    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									self.config_tester.run_common_tests()







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_A    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									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__     :			Union[str, Any]    ):
													UpperCAmelCase__    =							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__     :			Union[str, Any]    ):
													UpperCAmelCase__    =							[t.numpy() for t in outputs.encoder_attentions]
													UpperCAmelCase__    =							[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:
													UpperCAmelCase__    =							True
													UpperCAmelCase__    =							False
													UpperCAmelCase__    =							False
													UpperCAmelCase__    =							model_class(_A    )
													UpperCAmelCase__    =							model(self._prepare_for_class(_A					,_A    )    )
													UpperCAmelCase__    =							len(_A    )
													self.assertEqual(config.output_hidden_states					,_A    )
													check_encoder_attentions_output(_A    )

													if self.is_encoder_decoder:
																	UpperCAmelCase__    =							model_class(_A    )
																	UpperCAmelCase__    =							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"]
													UpperCAmelCase__    =							True
													UpperCAmelCase__    =							model_class(_A    )
													UpperCAmelCase__    =							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
													UpperCAmelCase__    =							True
													UpperCAmelCase__    =							True
													UpperCAmelCase__    =							model_class(_A    )
													UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			int    ):
									pass







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									pass






def 						a_		(      lowerCamelCase ):
				return tf.constant(_lowerCamelCase      ,    dtype=tf.intaa )


lowerCAmelCase__						:						Optional[int]		  =			1E-4







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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'    ).led

									# change to intended input here
									UpperCAmelCase__    =							_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]    )
									UpperCAmelCase__    =							_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]    )
									UpperCAmelCase__    =							prepare_led_inputs_dict(model.config					,_A					,_A    )
									UpperCAmelCase__    =							model(**_A    )[0]
									UpperCAmelCase__    =							(1, 1_024, 768)
									self.assertEqual(output.shape					,_A    )
									# change to expected output here
									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'    )

									# change to intended input here
									UpperCAmelCase__    =							_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]    )
									UpperCAmelCase__    =							_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]    )
									UpperCAmelCase__    =							prepare_led_inputs_dict(model.config					,_A					,_A    )
									UpperCAmelCase__    =							model(**_A    )[0]
									UpperCAmelCase__    =							(1, 1_024, model.config.vocab_size)
									self.assertEqual(output.shape					,_A    )
									# change to expected output here
									UpperCAmelCase__    =							tf.convert_to_tensor(
									    [[33.6_507, 6.4_5_7_2, 16.8_089], [5.8_7_3_9, -2.4_2_3_8, 11.2_902], [-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 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return x if y == 0 else greatest_common_divisor(lowerCamelCase      ,    x % y )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return (x * y) // greatest_common_divisor(lowerCamelCase      ,    lowerCamelCase )



def 						a_		(      lowerCamelCase = 2_0 ):
				UpperCAmelCase__    =							1
				for i in range(1      ,    n + 1 ):
								UpperCAmelCase__    =							lcm(lowerCamelCase      ,    lowerCamelCase )
				return g


if __name__ == "__main__":
						print(F"""{solution() = }""")



