from bisect import bisect
from itertools import accumulate





def 		_lowerCAmelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Any					):
					SCREAMING_SNAKE_CASE           =    sorted(zip(UpperCAmelCase__ ,	UpperCAmelCase__					) ,	key=lambda UpperCAmelCase__					: x[0] / x[1] ,	reverse=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    [i[0] for i in r], [i[1] for i in r]
					SCREAMING_SNAKE_CASE           =    list(accumulate(UpperCAmelCase__					)					)
					SCREAMING_SNAKE_CASE           =    bisect(UpperCAmelCase__ ,	UpperCAmelCase__					)
					return (
					    0
					    if k == 0
					    else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
					    if k != n
					    else sum(vl[:k]					)
					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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      :
							lowercase__  :    Dict									=							LEDConfig
							lowercase__  :    List[str]									=							{}
							lowercase__  :    Union[str, Any]									=							"""gelu"""




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Dict=13		,							_UpperCamelCase				:   Optional[int]=7		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   List[Any]=False		,							_UpperCamelCase				:   Dict=99		,							_UpperCamelCase				:   Optional[Any]=32		,							_UpperCamelCase				:   Any=2		,							_UpperCamelCase				:   List[str]=4		,							_UpperCamelCase				:   Union[str, Any]=37		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   List[Any]=0.1		,							_UpperCamelCase				:   Union[str, Any]=20		,							_UpperCamelCase				:   str=2		,							_UpperCamelCase				:   Optional[Any]=1		,							_UpperCamelCase				:   Optional[int]=0		,							_UpperCamelCase				:   int=4		,							)   ->			str:



												'''simple docstring'''

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

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




							def 		__snake_case(  self				:   int )   ->			Tuple:



												'''simple docstring'''

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

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

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





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModel(config=_UpperCamelCase ).get_decoder()
												SCREAMING_SNAKE_CASE           =    inputs_dict["input_ids"]

												SCREAMING_SNAKE_CASE           =    input_ids[:1, :]
												SCREAMING_SNAKE_CASE           =    inputs_dict["attention_mask"][:1, :]
												SCREAMING_SNAKE_CASE           =    1

												# first forward pass
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    outputs.to_tuple()

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

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

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

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[str]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	UpperCAmelCase__       :							Optional[int]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	):
					if attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.cast(tf.math.not_equal(UpperCAmelCase__ ,	config.pad_token_id					) ,	tf.inta					)
					if decoder_attention_mask is None:
										SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
					if decoder_head_mask is None:
										SCREAMING_SNAKE_CASE           =    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      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Optional[int]									=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							lowercase__  :    List[Any]									=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
							lowercase__  :    int									=							(
							    {
							        """conversational""": TFLEDForConditionalGeneration,
							        """feature-extraction""": TFLEDModel,
							        """summarization""": TFLEDForConditionalGeneration,
							        """text2text-generation""": TFLEDForConditionalGeneration,
							        """translation""": TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__  :    List[Any]									=							True
							lowercase__  :    List[str]									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Union[str, Any]									=							False




							def 		__snake_case(  self				:   Tuple )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   List[str] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			str:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    self.model_tester.seq_length
												SCREAMING_SNAKE_CASE           =    self.model_tester.encoder_seq_length

												def check_decoder_attentions_output(_UpperCamelCase				:   Dict ):
																	SCREAMING_SNAKE_CASE           =    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(_UpperCamelCase				:   Optional[Any] ):
																	SCREAMING_SNAKE_CASE           =    [t.numpy() for t in outputs.encoder_attentions]
																	SCREAMING_SNAKE_CASE           =    [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:
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase )
																	self.assertEqual(config.output_hidden_states		,							_UpperCamelCase )
																	check_encoder_attentions_output(_UpperCamelCase )

																	if self.is_encoder_decoder:
																						SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    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"]
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    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
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    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				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

												pass





							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												pass





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					return tf.constant(UpperCAmelCase__ ,	dtype=tf.intaa					)


_lowerCamelCase   :				str     			=						1e-4


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




							def 		__snake_case(  self				:   int )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, 768)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    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]		,							_UpperCamelCase		,							atol=1e-3 )





							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, model.config.vocab_size)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    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]		,							_UpperCamelCase		,							atol=1e-3		,							rtol=1e-3 )




