

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_   :
      UpperCamelCase_      :		Tuple										=    LEDConfig
      UpperCamelCase_      :		Dict										=    {}
      UpperCamelCase_      :		Optional[int]										=    "gelu"



      def __init__(      self				:    Tuple      , A__				:    List[Any]      , A__				:    Union[str, Any]=13      , A__				:    Optional[int]=7      , A__				:    Optional[Any]=True      , A__				:    Any=False      , A__				:    Union[str, Any]=99      , A__				:    Optional[Any]=32      , A__				:    Tuple=2      , A__				:    Optional[int]=4      , A__				:    Optional[int]=37      , A__				:    Any=0.1      , A__				:    List[Any]=0.1      , A__				:    str=20      , A__				:    Union[str, Any]=2      , A__				:    int=1      , A__				:    Dict=0      , A__				:    Optional[int]=4      , )   ->					List[Any]:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							seq_length
        _snake_case						=							is_training
        _snake_case						=							use_labels
        _snake_case						=							vocab_size
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							intermediate_size

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



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					List[Any]:
        _snake_case						=							ids_tensor([self.batch_size, self.seq_length - 1]      , self.vocab_size		)
        _snake_case						=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)      , 1		)
        _snake_case						=							tf.concat([input_ids, eos_tensor]      , axis=1		)

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

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







      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    Union[str, Any]      , A__				:    Optional[Any]		)   ->					Dict:
        _snake_case						=							TFLEDModel(config=A__		).get_decoder()
        _snake_case						=							inputs_dict['''input_ids''']

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

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

        _snake_case,					_snake_case						=							outputs.to_tuple()

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

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

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

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=None			,	_UpperCamelCase=None			,	_UpperCamelCase=None			,	_UpperCamelCase=None			,	)							->     List[Any]:



  """simple docstring"""






  if attention_mask is None:
    _snake_case						=							tf.cast(tf.math.not_equal(_UpperCamelCase			,	config.pad_token_id					)			,	tf.inta					)
  if decoder_attention_mask is None:
    _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:
    _snake_case						=							tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    _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_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		str										=    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      UpperCamelCase_      :		List[Any]										=    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
      UpperCamelCase_      :		Tuple										=    (
          {
              "conversational": TFLEDForConditionalGeneration,
              "feature-extraction": TFLEDModel,
              "summarization": TFLEDForConditionalGeneration,
              "text2text-generation": TFLEDForConditionalGeneration,
              "translation": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase_      :		List[Any]										=    True
      UpperCamelCase_      :		List[str]										=    False
      UpperCamelCase_      :		Any										=    False
      UpperCamelCase_      :		Any										=    False



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Tuple:
        _snake_case						=							TFLEDModelTester(self		)
        _snake_case						=							ConfigTester(self      , config_class=A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        self.config_tester.run_common_tests()



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Tuple:
        _snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Union[str, Any]:
        _snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case						=							tf.zeros_like(inputs_dict['''attention_mask''']		)
        _snake_case						=							2
        _snake_case						=							tf.where(
            tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices      , 1      , inputs_dict['''global_attention_mask''']      , )

        _snake_case						=							True
        _snake_case						=							self.model_tester.seq_length
        _snake_case						=							self.model_tester.encoder_seq_length

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

          if self.is_encoder_decoder:
            _snake_case						=							model_class(A__		)
            _snake_case						=							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"]
          _snake_case						=							True
          _snake_case						=							model_class(A__		)
          _snake_case						=							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
          _snake_case						=							True
          _snake_case						=							True
          _snake_case						=							model_class(A__		)
          _snake_case						=							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 						UpperCamelCase_		(      self				:    Any		)   ->					str:
        pass







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



def 				snake_case_(_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






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


__A      		=     1e-4



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



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        _snake_case						=							TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		).led

        # change to intended input here
        _snake_case						=							_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        _snake_case						=							_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        _snake_case						=							prepare_led_inputs_dict(model.config      , A__      , A__		)
        _snake_case						=							model(**A__		)[0]
        _snake_case						=							(1, 1024, 768)
        self.assertEqual(output.shape      , A__		)
        # change to expected output here
        _snake_case						=							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]      , A__      , atol=1e-3		)







      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					str:
        _snake_case						=							TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		)

        # change to intended input here
        _snake_case						=							_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        _snake_case						=							_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]		)
        _snake_case						=							prepare_led_inputs_dict(model.config      , A__      , A__		)
        _snake_case						=							model(**A__		)[0]
        _snake_case						=							(1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape      , A__		)
        # change to expected output here
        _snake_case						=							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]      , A__      , atol=1e-3      , rtol=1e-3		)




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

