


'''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_    :





    """simple docstring"""






    lowerCamelCase    :   Optional[int]					=     LEDConfig
    lowerCamelCase    :   List[str]					=     {}
    lowerCamelCase    :   str					=     '''gelu'''







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

         __lowerCamelCase  :      Union[str, Any]														=						hidden_dropout_prob
         __lowerCamelCase  :      Any														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[Any]														=						max_position_embeddings
         __lowerCamelCase  :      Union[str, Any]														=						eos_token_id
         __lowerCamelCase  :      Union[str, Any]														=						pad_token_id
         __lowerCamelCase  :      Optional[Any]														=						bos_token_id
         __lowerCamelCase  :      Optional[Any]														=						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  :      Any														=						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  :      Tuple														=						(
             self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
         )







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

         __lowerCamelCase  :      Union[str, Any]														=						ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

         __lowerCamelCase  :      Any														=						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  :      Dict														=						prepare_led_inputs_dict(_lowercase     ,	_lowercase     ,	_lowercase      )
         __lowerCamelCase  :      Tuple														=						tf.concat(
             [tf.zeros_like(_lowercase      )[:, :-1], tf.ones_like(_lowercase      )[:, -1:]]     ,	axis=-1     ,	)
         __lowerCamelCase  :      List[Any]														=						global_attention_mask
         return config, inputs_dict







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[Any]														=						TFLEDModel(config=_lowercase      ).get_decoder()
         __lowerCamelCase  :      Optional[int]														=						inputs_dict['''input_ids''']

         __lowerCamelCase  :      Dict														=						input_ids[:1, :]
         __lowerCamelCase  :      Optional[Any]														=						inputs_dict['''attention_mask'''][:1, :]
         __lowerCamelCase  :      Optional[int]														=						1

         # first forward pass
         __lowerCamelCase  :      Optional[Any]														=						model(_lowercase     ,	attention_mask=_lowercase     ,	use_cache=_lowercase      )

         __lowerCamelCase  :      Union[str, Any]														=						outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __lowerCamelCase  :      Any														=						ids_tensor((self.batch_size, 3)     ,	config.vocab_size      )
         __lowerCamelCase  :      List[str]														=						tf.cast(ids_tensor((self.batch_size, 3)     ,	2      )     ,	tf.inta      )

         # append to next input_ids and
         __lowerCamelCase  :      Optional[int]														=						tf.concat([input_ids, next_tokens]     ,	axis=-1      )
         __lowerCamelCase  :      Union[str, Any]														=						tf.concat([attention_mask, next_attn_mask]     ,	axis=-1      )

         __lowerCamelCase  :      str														=						model(_lowercase     ,	attention_mask=_lowercase      )[0]
         __lowerCamelCase  :      Optional[int]														=						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
         __lowerCamelCase  :      Dict														=						int(ids_tensor((1,)     ,	output_from_past.shape[-1]      )      )
         __lowerCamelCase  :      Any														=						output_from_no_past[:, -3:, random_slice_idx]
         __lowerCamelCase  :      Optional[Any]														=						output_from_past[:, :, random_slice_idx]

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







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							int=None ,       UpperCAmelCase_       :							int=None ,       UpperCAmelCase_       :							List[str]=None ,       UpperCAmelCase_       :							List[str]=None ,       )	->						List[str]:
     if attention_mask is None:
          __lowerCamelCase  :      Optional[Any]														=						tf.cast(tf.math.not_equal(UpperCAmelCase_ ,       config.pad_token_id				) ,       tf.inta				)
     if decoder_attention_mask is None:
          __lowerCamelCase  :      int														=						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  :      str														=						tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
     if decoder_head_mask is None:
          __lowerCamelCase  :      Optional[Any]														=						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_    (__a   ,						__a   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[int]					=     (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    lowerCamelCase    :   List[str]					=     (TFLEDForConditionalGeneration,) if is_tf_available() else ()
    lowerCamelCase    :   Union[str, Any]					=     (
        {
            '''conversational''': TFLEDForConditionalGeneration,
            '''feature-extraction''': TFLEDModel,
            '''summarization''': TFLEDForConditionalGeneration,
            '''text2text-generation''': TFLEDForConditionalGeneration,
            '''translation''': TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowerCamelCase    :   str					=     True
    lowerCamelCase    :   Any					=     False
    lowerCamelCase    :   Union[str, Any]					=     False
    lowerCamelCase    :   Any					=     False







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      Dict														=						TFLEDModelTester(self      )
         __lowerCamelCase  :      Any														=						ConfigTester(self     ,	config_class=_lowercase      )







    def  lowercase_							(				self      )							->	List[str]:
         self.config_tester.run_common_tests()







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*_lowercase      )







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

         __lowerCamelCase  :      int														=						True
         __lowerCamelCase  :      str														=						self.model_tester.seq_length
         __lowerCamelCase  :      Dict														=						self.model_tester.encoder_seq_length

         def check_decoder_attentions_output(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Tuple														=						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(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      str														=						[t.numpy() for t in outputs.encoder_attentions]
              __lowerCamelCase  :      Any														=						[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:
              __lowerCamelCase  :      Optional[int]														=						True
              __lowerCamelCase  :      List[Any]														=						False
              __lowerCamelCase  :      Optional[int]														=						False
              __lowerCamelCase  :      Union[str, Any]														=						model_class(_lowercase      )
              __lowerCamelCase  :      Optional[int]														=						model(self._prepare_for_class(_lowercase     ,	_lowercase      )      )
              __lowerCamelCase  :      List[str]														=						len(_lowercase      )
              self.assertEqual(config.output_hidden_states     ,	_lowercase      )
              check_encoder_attentions_output(_lowercase      )

              if self.is_encoder_decoder:
                   __lowerCamelCase  :      Optional[int]														=						model_class(_lowercase      )
                   __lowerCamelCase  :      Union[str, Any]														=						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"]
              __lowerCamelCase  :      Union[str, Any]														=						True
              __lowerCamelCase  :      List[str]														=						model_class(_lowercase      )
              __lowerCamelCase  :      Dict														=						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
              __lowerCamelCase  :      str														=						True
              __lowerCamelCase  :      Dict														=						True
              __lowerCamelCase  :      int														=						model_class(_lowercase      )
              __lowerCamelCase  :      Optional[int]														=						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  lowercase_							(				self      )							->	Optional[Any]:
         pass







    def  lowercase_							(				self      )							->	List[Any]:
         pass







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						List[str]:
     return tf.constant(UpperCAmelCase_ ,       dtype=tf.intaa				)


A__						:   Dict				=    1e-4





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





    """simple docstring"""






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

         # change to intended input here
         __lowerCamelCase  :      List[str]														=						_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]      )
         __lowerCamelCase  :      int														=						_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]      )
         __lowerCamelCase  :      Union[str, Any]														=						prepare_led_inputs_dict(model.config     ,	_lowercase     ,	_lowercase      )
         __lowerCamelCase  :      Optional[Any]														=						model(**_lowercase      )[0]
         __lowerCamelCase  :      Optional[int]														=						(1, 10_24, 7_68)
         self.assertEqual(output.shape     ,	_lowercase      )
         # change to expected output here
         __lowerCamelCase  :      Optional[Any]														=						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]     ,	_lowercase     ,	atol=1E-3      )







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

         # change to intended input here
         __lowerCamelCase  :      Dict														=						_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]      )
         __lowerCamelCase  :      List[str]														=						_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]      )
         __lowerCamelCase  :      Tuple														=						prepare_led_inputs_dict(model.config     ,	_lowercase     ,	_lowercase      )
         __lowerCamelCase  :      Any														=						model(**_lowercase      )[0]
         __lowerCamelCase  :      Dict														=						(1, 10_24, model.config.vocab_size)
         self.assertEqual(output.shape     ,	_lowercase      )
         # change to expected output here
         __lowerCamelCase  :      Optional[Any]														=						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]     ,	_lowercase     ,	atol=1E-3     ,	rtol=1E-3      )







import torch




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 if torch.cuda.is_available():
  _UpperCamelCase:  Any    						=			torch.cuda.device_count()
 else:
  _UpperCamelCase:  Union[str, Any]    						=			0
 print(F"""Successfully ran on {num_gpus} GPUs"""		)


if __name__ == "__main__":
  main()