

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  A						:







    """simple docstring"""





    lowerCamelCase			=							LEDConfig
    lowerCamelCase			=							{}
    lowerCamelCase			=							'gelu'


    def __init__(      self :       str,lowercase_ :       Any,lowercase_ :       int=1_3,lowercase_ :       Any=7,lowercase_ :       List[str]=True,lowercase_ :       Tuple=False,lowercase_ :       List[Any]=9_9,lowercase_ :       Dict=3_2,lowercase_ :       Tuple=2,lowercase_ :       str=4,lowercase_ :       Optional[int]=3_7,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[Any]=0.1,lowercase_ :       List[str]=2_0,lowercase_ :       List[str]=2,lowercase_ :       Tuple=1,lowercase_ :       List[Any]=0,lowercase_ :       int=4,)->    Dict:





        '''simple docstring'''







        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 						snake_case__     (      self :       Dict		)->    str:





        '''simple docstring'''







        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(lowercase_,lowercase_,lowercase_		)
        A__          =   tf.concat(
            [tf.zeros_like(lowercase_		)[:, :-1], tf.ones_like(lowercase_		)[:, -1:]],axis=-1,)
        A__          =   global_attention_mask
        return config, inputs_dict


    def 						snake_case__     (      self :       Dict,lowercase_ :       Optional[Any],lowercase_ :       Any		)->    int:





        '''simple docstring'''







        A__          =   TFLEDModel(config=lowercase_		).get_decoder()
        A__          =   inputs_dict['input_ids']

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

        # first forward pass
        A__          =   model(lowercase_,attention_mask=lowercase_,use_cache=lowercase_		)

        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(lowercase_,attention_mask=lowercase_		)[0]
        A__          =   model(lowercase_,attention_mask=lowercase_,past_key_values=lowercase_		)[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(lowercase_,lowercase_,rtol=1E-3		)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=None    ,			SCREAMING_SNAKE_CASE__			:	Any=None    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=None    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=None    ,			)							->      Any:





    '''simple docstring'''






    if attention_mask is None:
        A__          =   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__    ,			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  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





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


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   TFLEDModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Optional[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase_		)


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





        '''simple docstring'''







        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(lowercase_ :       int		):
            A__          =   outputs.decoder_attentions
            self.assertEqual(len(lowercase_		),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(lowercase_ :       List[str]		):
            A__          =   [t.numpy() for t in outputs.encoder_attentions]
            A__          =   [t.numpy() for t in outputs.encoder_global_attentions]
            self.assertEqual(len(lowercase_		),self.model_tester.num_hidden_layers		)
            self.assertEqual(len(lowercase_		),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(lowercase_		)
            A__          =   model(self._prepare_for_class(lowercase_,lowercase_		)		)
            A__          =   len(lowercase_		)
            self.assertEqual(config.output_hidden_states,lowercase_		)
            check_encoder_attentions_output(lowercase_		)

            if self.is_encoder_decoder:
                A__          =   model_class(lowercase_		)
                A__          =   model(self._prepare_for_class(lowercase_,lowercase_		)		)
                self.assertEqual(config.output_hidden_states,lowercase_		)
                check_decoder_attentions_output(lowercase_		)

            # Check that output attentions can also be changed via the config
            del inputs_dict["output_attentions"]
            A__          =   True
            A__          =   model_class(lowercase_		)
            A__          =   model(self._prepare_for_class(lowercase_,lowercase_		)		)
            self.assertEqual(config.output_hidden_states,lowercase_		)
            check_encoder_attentions_output(lowercase_		)

            # Check attention is always last and order is fine
            A__          =   True
            A__          =   True
            A__          =   model_class(lowercase_		)
            A__          =   model(self._prepare_for_class(lowercase_,lowercase_		)		)

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


    @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'		)
    def 						snake_case__     (      self :       Optional[int]		)->    Dict:





        '''simple docstring'''







        pass


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





        '''simple docstring'''







        pass




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict      )							->      Any:





    '''simple docstring'''






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


lowercase_       		=  1e-4


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







    """simple docstring"""





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





        '''simple docstring'''







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

        # change to intended input here
        A__          =   _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
        A__          =   _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
        A__          =   prepare_led_inputs_dict(model.config,lowercase_,lowercase_		)
        A__          =   model(**lowercase_		)[0]
        A__          =   (1, 1_0_2_4, 7_6_8)
        self.assertEqual(output.shape,lowercase_		)
        # change to expected output here
        A__          =   tf.convert_to_tensor(
            [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]],)
        tf.debugging.assert_near(output[:, :3, :3],lowercase_,atol=1E-3		)


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





        '''simple docstring'''







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

        # change to intended input here
        A__          =   _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
        A__          =   _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]		)
        A__          =   prepare_led_inputs_dict(model.config,lowercase_,lowercase_		)
        A__          =   model(**lowercase_		)[0]
        A__          =   (1, 1_0_2_4, model.config.vocab_size)
        self.assertEqual(output.shape,lowercase_		)
        # change to expected output here
        A__          =   tf.convert_to_tensor(
            [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]],)
        tf.debugging.assert_near(output[:, :3, :3],lowercase_,atol=1E-3,rtol=1E-3		)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[0] * (w + 1) for _ in range(n + 1      )]

    for i in range(1    ,			n + 1      ):
        for w_ in range(1    ,			w + 1      ):
            if wt[i - 1] <= w_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            'The number of weights must be the same as the number of values.\n'
            f'But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__      )} values'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )
    for i in range(SCREAMING_SNAKE_CASE__      ):
        if not isinstance(wt[i]    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowercase_		,			lowercase_       		=  knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)

