






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'''
     SCREAMING_SNAKE_CASE     =					LEDConfig
     SCREAMING_SNAKE_CASE     =					{}
     SCREAMING_SNAKE_CASE     =					"gelu"




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


         """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 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """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(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						tf.concat(
             [tf.zeros_like(UpperCamelCase_					)[:, :-1], tf.ones_like(UpperCamelCase_					)[:, -1:]]			,						axis=-1			,						)
         __A       =						global_attention_mask
         return config, inputs_dict





     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         __A       =						TFLEDModel(config=UpperCamelCase_					).get_decoder()
         __A       =						inputs_dict["""input_ids"""]

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

         # first forward pass
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						use_cache=UpperCamelCase_					)

         __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(UpperCamelCase_			,						attention_mask=UpperCamelCase_					)[0]
         __A       =						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
         __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(UpperCamelCase_			,						UpperCamelCase_			,						rtol=1e-3					)



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[int]      ,					__lowercase :      Union[str, Any]      ,					__lowercase :      Union[str, Any]      ,					__lowercase :      Tuple=None      ,					__lowercase :      List[str]=None      ,					__lowercase :      List[str]=None      ,					__lowercase :      Dict=None      ,					)    ->   int:





    """simple docstring"""

    if attention_mask is None:
        __A       =						tf.cast(tf.math.not_equal(__lowercase      ,					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    __lowercase   (						lowercase_      ,   lowercase_      ,   unittest.TestCase	):







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




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						TFLEDModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """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(UpperCamelCase_							:      Dict					):
             __A       =						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_							:      int					):
             __A       =						[t.numpy() for t in outputs.encoder_attentions]
             __A       =						[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:
             __A       =						True
             __A       =						False
             __A       =						False
             __A       =						model_class(UpperCamelCase_					)
             __A       =						model(self._prepare_for_class(UpperCamelCase_			,						UpperCamelCase_					)					)
             __A       =						len(UpperCamelCase_					)
             self.assertEqual(config.output_hidden_states			,						UpperCamelCase_					)
             check_encoder_attentions_output(UpperCamelCase_					)

             if self.is_encoder_decoder:
                 __A       =						model_class(UpperCamelCase_					)
                 __A       =						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"]
             __A       =						True
             __A       =						model_class(UpperCamelCase_					)
             __A       =						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
             __A       =						True
             __A       =						True
             __A       =						model_class(UpperCamelCase_					)
             __A       =						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 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         pass





     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         pass



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[int]     )    ->   Dict:





    """simple docstring"""

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


__a   :							List[Any]						= 1e-4






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







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



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

         # change to intended input here
         __A       =						_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]					)
         __A       =						_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]					)
         __A       =						prepare_led_inputs_dict(model.config			,						UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						model(**UpperCamelCase_					)[0]
         __A       =						(1, 1_024, 768)
         self.assertEqual(output.shape			,						UpperCamelCase_					)
         # change to expected output here
         __A       =						tf.convert_to_tensor(
             [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]			,						)
         tf.debugging.assert_near(output[:, :3, :3]			,						UpperCamelCase_			,						atol=1e-3					)





     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



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

         # change to intended input here
         __A       =						_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]					)
         __A       =						_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]					)
         __A       =						prepare_led_inputs_dict(model.config			,						UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						model(**UpperCamelCase_					)[0]
         __A       =						(1, 1_024, model.config.vocab_size)
         self.assertEqual(output.shape			,						UpperCamelCase_					)
         # change to expected output here
         __A       =						tf.convert_to_tensor(
             [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]			,						)
         tf.debugging.assert_near(output[:, :3, :3]			,						UpperCamelCase_			,						atol=1e-3			,						rtol=1e-3					)












from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




