


'''simple docstring'''




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 		UpperCAmelCase_      :
     lowerCamelCase				:	Union[str, Any]		=     LEDConfig
     lowerCamelCase				:	Tuple		=     {}
     lowerCamelCase				:	Any		=     '''gelu'''





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Dict=1_3    ,      UpperCAmelCase__       :      Tuple=7    ,      UpperCAmelCase__       :      List[str]=True    ,      UpperCAmelCase__       :      int=False    ,      UpperCAmelCase__       :      Optional[int]=9_9    ,      UpperCAmelCase__       :      Tuple=3_2    ,      UpperCAmelCase__       :      Optional[Any]=2    ,      UpperCAmelCase__       :      int=4    ,      UpperCAmelCase__       :      str=3_7    ,      UpperCAmelCase__       :      Optional[int]=0.1    ,      UpperCAmelCase__       :      str=0.1    ,      UpperCAmelCase__       :      Dict=2_0    ,      UpperCAmelCase__       :      Tuple=2    ,      UpperCAmelCase__       :      Tuple=1    ,      UpperCAmelCase__       :      Dict=0    ,      UpperCAmelCase__       :      str=4    ,      )				->  Optional[Any]:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			seq_length
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size

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





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length - 1]    ,      self.vocab_size    )
        lowerCAmelCase     =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )    ,      1    )
        lowerCAmelCase     =			tf.concat([input_ids, eos_tensor]    ,      axis=1    )

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

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





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  Optional[int]:
        lowerCAmelCase     =			TFLEDModel(config=UpperCAmelCase__    ).get_decoder()
        lowerCAmelCase     =			inputs_dict['input_ids']

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

        # first forward pass
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      use_cache=UpperCAmelCase__    )

        lowerCAmelCase     ,  lowerCAmelCase     =			outputs.to_tuple()

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

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

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

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





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Any	, lowerCamelCase	:     List[Any]=None	, lowerCamelCase	:     Any=None	, lowerCamelCase	:     Union[str, Any]=None	, lowerCamelCase	:     List[str]=None	, ):
   if attention_mask is None:
      lowerCAmelCase     =			tf.cast(tf.math.not_equal(lowerCamelCase	, config.pad_token_id				)	, tf.inta				)
   if decoder_attention_mask is None:
      lowerCAmelCase     =			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:
      lowerCAmelCase     =			tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
   if decoder_head_mask is None:
      lowerCAmelCase     =			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 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
     lowerCamelCase				:	List[Any]		=     (TFLEDForConditionalGeneration,) if is_tf_available() else ()
     lowerCamelCase				:	str		=     (
         {
             '''conversational''': TFLEDForConditionalGeneration,
             '''feature-extraction''': TFLEDModel,
             '''summarization''': TFLEDForConditionalGeneration,
             '''text2text-generation''': TFLEDForConditionalGeneration,
             '''translation''': TFLEDForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     lowerCamelCase				:	Dict		=     True
     lowerCamelCase				:	List[Any]		=     False
     lowerCamelCase				:	List[Any]		=     False
     lowerCamelCase				:	List[str]		=     False





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Dict:
        lowerCAmelCase     =			TFLEDModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        self.config_tester.run_common_tests()





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Tuple:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase     =			tf.zeros_like(inputs_dict['attention_mask']    )
        lowerCAmelCase     =			2
        lowerCAmelCase     =			tf.where(
            tf.range(self.model_tester.seq_length    )[None, :] < num_global_attn_indices    ,      1    ,      inputs_dict['global_attention_mask']    ,      )

        lowerCAmelCase     =			True
        lowerCAmelCase     =			self.model_tester.seq_length
        lowerCAmelCase     =			self.model_tester.encoder_seq_length

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

           if self.is_encoder_decoder:
              lowerCAmelCase     =			model_class(UpperCAmelCase__    )
              lowerCAmelCase     =			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"]
           lowerCAmelCase     =			True
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			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
           lowerCAmelCase     =			True
           lowerCAmelCase     =			True
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      int    )				->  List[Any]:
        pass





     def 							__UpperCAmelCase      ( self       :      int    )				->  Any:
        # TODO: Head-masking not yet implement
        pass





def 	a_					( lowerCamelCase	:     Any				):
   return tf.constant(lowerCamelCase	, dtype=tf.intaa				)


__snake_case    						=1e-4




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





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        lowerCAmelCase     =			TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'    ).led

        # change to intended input here
        lowerCAmelCase     =			_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]]    )
        lowerCAmelCase     =			_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]]    )
        lowerCAmelCase     =			prepare_led_inputs_dict(model.config    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			model(**UpperCAmelCase__    )[0]
        lowerCAmelCase     =			(1, 1_0_2_4, 7_6_8)
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # change to expected output here
        lowerCAmelCase     =			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]    ,      UpperCAmelCase__    ,      atol=1E-3    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'    )

        # change to intended input here
        lowerCAmelCase     =			_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]]    )
        lowerCAmelCase     =			_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]]    )
        lowerCAmelCase     =			prepare_led_inputs_dict(model.config    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			model(**UpperCAmelCase__    )[0]
        lowerCAmelCase     =			(1, 1_0_2_4, model.config.vocab_size)
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # change to expected output here
        lowerCAmelCase     =			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]    ,      UpperCAmelCase__    ,      atol=1E-3    ,      rtol=1E-3    )









'''simple docstring'''




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))





