


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_       :
					UpperCAmelCase__ :   Union[str, Any]   				=      LEDConfig
					UpperCAmelCase__ :   Optional[Any]   				=      {}
					UpperCAmelCase__ :   str   				=      '''gelu'''
					def __init__(				self       :Tuple      ,__snake_case       :int      ,__snake_case       :List[str]=13      ,__snake_case       :Optional[Any]=7      ,__snake_case       :Any=True      ,__snake_case       :str=False      ,__snake_case       :str=99      ,__snake_case       :str=32      ,__snake_case       :Union[str, Any]=2      ,__snake_case       :int=4      ,__snake_case       :List[str]=37      ,__snake_case       :Optional[int]=0.1      ,__snake_case       :Optional[int]=0.1      ,__snake_case       :List[str]=20      ,__snake_case       :Any=2      ,__snake_case       :Any=1      ,__snake_case       :Dict=0      ,__snake_case       :Optional[Any]=4      ,) ->   List[Any]:
												a__					=					parent
												a__					=					batch_size
												a__					=					seq_length
												a__					=					is_training
												a__					=					use_labels
												a__					=					vocab_size
												a__					=					hidden_size
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					intermediate_size

												a__					=					hidden_dropout_prob
												a__					=					attention_probs_dropout_prob
												a__					=					max_position_embeddings
												a__					=					eos_token_id
												a__					=					pad_token_id
												a__					=					bos_token_id
												a__					=					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
												a__					=					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
												a__					=					(
												    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
												)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   str:
												a__					=					ids_tensor([self.batch_size, self.seq_length - 1]      ,self.vocab_size				)
												a__					=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)      ,1				)
												a__					=					tf.concat([input_ids, eos_tensor]      ,axis=1				)

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

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



					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[Any]      ,__snake_case       :Tuple				) ->   Dict:
												a__					=					TFLEDModel(config=__snake_case				).get_decoder()
												a__					=					inputs_dict['input_ids']

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

												# first forward pass
												a__					=					model(__snake_case      ,attention_mask=__snake_case      ,use_cache=__snake_case				)

												a__				, a__					=					outputs.to_tuple()

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

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

												a__					=					model(__snake_case      ,attention_mask=__snake_case				)[0]
												a__					=					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
												a__					=					int(ids_tensor((1,)      ,output_from_past.shape[-1]				)				)
												a__					=					output_from_no_past[:, -3:, random_slice_idx]
												a__					=					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 							__lowercase       (			__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str=None ,   __lowerCAmelCase							:    List[str]=None ,   __lowerCAmelCase							:    List[Any]=None ,   __lowerCAmelCase							:    str=None ,   ):
							if attention_mask is None:
														a__					=					tf.cast(tf.math.not_equal(__lowerCAmelCase ,   config.pad_token_id       ) ,   tf.inta       )
							if decoder_attention_mask is None:
														a__					=					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:
														a__					=					tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
							if decoder_head_mask is None:
														a__					=					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_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Any   				=      (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					UpperCAmelCase__ :   List[Any]   				=      (TFLEDForConditionalGeneration,) if is_tf_available() else ()
					UpperCAmelCase__ :   Optional[int]   				=      (
					    {
					        '''conversational''': TFLEDForConditionalGeneration,
					        '''feature-extraction''': TFLEDModel,
					        '''summarization''': TFLEDForConditionalGeneration,
					        '''text2text-generation''': TFLEDForConditionalGeneration,
					        '''translation''': TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__ :   Any   				=      True
					UpperCAmelCase__ :   int   				=      False
					UpperCAmelCase__ :   str   				=      False
					UpperCAmelCase__ :   List[Any]   				=      False
					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					TFLEDModelTester(self				)
												a__					=					ConfigTester(self      ,config_class=__snake_case				)
					def 			lowerCamelCase__(				self       :int				) ->   List[str]:
												self.config_tester.run_common_tests()
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Any:
												a__					=					self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()
												a__					=					tf.zeros_like(inputs_dict['attention_mask']				)
												a__					=					2
												a__					=					tf.where(
												    tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices      ,1      ,inputs_dict['global_attention_mask']      ,)

												a__					=					True
												a__					=					self.model_tester.seq_length
												a__					=					self.model_tester.encoder_seq_length

												def check_decoder_attentions_output(__snake_case       :Tuple				):
																			a__					=					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(__snake_case       :str				):
																			a__					=					[t.numpy() for t in outputs.encoder_attentions]
																			a__					=					[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:
																			a__					=					True
																			a__					=					False
																			a__					=					False
																			a__					=					model_class(__snake_case				)
																			a__					=					model(self._prepare_for_class(__snake_case      ,__snake_case				)				)
																			a__					=					len(__snake_case				)
																			self.assertEqual(config.output_hidden_states      ,__snake_case				)
																			check_encoder_attentions_output(__snake_case				)

																			if self.is_encoder_decoder:
																										a__					=					model_class(__snake_case				)
																										a__					=					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"]
																			a__					=					True
																			a__					=					model_class(__snake_case				)
																			a__					=					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
																			a__					=					True
																			a__					=					True
																			a__					=					model_class(__snake_case				)
																			a__					=					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       :Any				) ->   Optional[Any]:
												pass



					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[Any]:
												# TODO: Head-masking not yet implement
												pass






def 							__lowercase       (			__lowerCAmelCase							:    Dict       ):
							return tf.constant(__lowerCAmelCase ,   dtype=tf.intaa       )


snake_case							:	int					      =				1e-4







@slow
@require_tf
class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												a__					=					TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'				).led

												# change to intended input here
												a__					=					_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
												a__					=					_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
												a__					=					prepare_led_inputs_dict(model.config      ,__snake_case      ,__snake_case				)
												a__					=					model(**__snake_case				)[0]
												a__					=					(1, 10_24, 7_68)
												self.assertEqual(output.shape      ,__snake_case				)
												# change to expected output here
												a__					=					tf.convert_to_tensor(
												    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]      ,)
												tf.debugging.assert_near(output[:, :3, :3]      ,__snake_case      ,atol=1E-3				)



					def 			lowerCamelCase__(				self       :int				) ->   List[str]:
												a__					=					TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'				)

												# change to intended input here
												a__					=					_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
												a__					=					_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
												a__					=					prepare_led_inputs_dict(model.config      ,__snake_case      ,__snake_case				)
												a__					=					model(**__snake_case				)[0]
												a__					=					(1, 10_24, model.config.vocab_size)
												self.assertEqual(output.shape      ,__snake_case				)
												# change to expected output here
												a__					=					tf.convert_to_tensor(
												    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]      ,)
												tf.debugging.assert_near(output[:, :3, :3]      ,__snake_case      ,atol=1E-3      ,rtol=1E-3				)








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




