





'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       GPTNeoXForCausalLM,
       GPTNeoXForQuestionAnswering,
       GPTNeoXForSequenceClassification,
       GPTNeoXForTokenClassification,
       GPTNeoXModel,
   )





class lowerCamelCase						:







  def __init__(		self     ,     lowercase__     ,     lowercase__=13     ,     lowercase__=7     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=99     ,     lowercase__=64     ,     lowercase__=5     ,     lowercase__=4     ,     lowercase__=37     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=512     ,     lowercase__=16     ,     lowercase__=2     ,     lowercase__=0.02     ,     lowercase__=3     ,     lowercase__=4     ,     lowercase__=None     ,     )		->						int:






        """simple docstring"""





        _snake_case		:	Any					     =     parent
        _snake_case		:	Any					     =     batch_size
        _snake_case		:	List[Any]					     =     seq_length
        _snake_case		:	Optional[int]					     =     is_training
        _snake_case		:	str					     =     use_input_mask
        _snake_case		:	Union[str, Any]					     =     use_token_type_ids
        _snake_case		:	Union[str, Any]					     =     use_labels
        _snake_case		:	List[str]					     =     vocab_size
        _snake_case		:	Union[str, Any]					     =     hidden_size
        _snake_case		:	Tuple					     =     num_hidden_layers
        _snake_case		:	List[str]					     =     num_attention_heads
        _snake_case		:	Any					     =     intermediate_size
        _snake_case		:	List[Any]					     =     hidden_act
        _snake_case		:	Optional[Any]					     =     hidden_dropout_prob
        _snake_case		:	Union[str, Any]					     =     attention_probs_dropout_prob
        _snake_case		:	List[Any]					     =     max_position_embeddings
        _snake_case		:	int					     =     type_vocab_size
        _snake_case		:	Union[str, Any]					     =     type_sequence_label_size
        _snake_case		:	Dict					     =     initializer_range
        _snake_case		:	Any					     =     num_labels
        _snake_case		:	List[str]					     =     num_choices
        _snake_case		:	List[Any]					     =     scope
        _snake_case		:	Dict					     =     vocab_size - 1







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





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

        _snake_case		:	Dict					     =     None
        if self.use_input_mask:
              _snake_case		:	List[Any]					     =     random_attention_mask([self.batch_size, self.seq_length]		)

        _snake_case		:	Any					     =     None
        if self.use_labels:
              _snake_case		:	Dict					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.num_labels		)

        _snake_case		:	Dict					     =     self.get_config()

        return config, input_ids, input_mask, token_labels







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        return GPTNeoXConfig(
            vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_hidden_layers=self.num_hidden_layers     ,     num_attention_heads=self.num_attention_heads     ,     intermediate_size=self.intermediate_size     ,     hidden_act=self.hidden_act     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     max_position_embeddings=self.max_position_embeddings     ,     type_vocab_size=self.type_vocab_size     ,     is_decoder=lowercase__     ,     initializer_range=self.initializer_range     ,     pad_token_id=self.pad_token_id     ,     )







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self.prepare_config_and_inputs()

        _snake_case		:	List[str]					     =     True

        return config, input_ids, input_mask, token_labels







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     GPTNeoXModel(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	List[str]					     =     model(lowercase__     ,     attention_mask=lowercase__		)
        _snake_case		:	List[str]					     =     model(lowercase__		)
        self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     True
        _snake_case		:	int					     =     GPTNeoXModel(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	str					     =     model(lowercase__     ,     attention_mask=lowercase__		)
        self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     GPTNeoXForCausalLM(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	List[str]					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.num_labels
        _snake_case		:	Dict					     =     GPTNeoXForQuestionAnswering(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	int					     =     model(lowercase__     ,     attention_mask=lowercase__		)
        self.parent.assertEqual(result.start_logits.shape     ,     (self.batch_size, self.seq_length)		)
        self.parent.assertEqual(result.end_logits.shape     ,     (self.batch_size, self.seq_length)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.num_labels
        _snake_case		:	Any					     =     GPTNeoXForSequenceClassification(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	List[str]					     =     ids_tensor([self.batch_size]     ,     self.type_sequence_label_size		)
        _snake_case		:	Union[str, Any]					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.num_labels
        _snake_case		:	List[Any]					     =     GPTNeoXForTokenClassification(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Any					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.num_labels)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	Dict					     =     True
        _snake_case		:	Dict					     =     GPTNeoXForCausalLM(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()

        # first forward pass
        _snake_case		:	Tuple					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     use_cache=lowercase__		)
        _snake_case		:	Optional[int]					     =     outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        _snake_case		:	Any					     =     ids_tensor((self.batch_size, 3)     ,     config.vocab_size		)
        _snake_case		:	List[Any]					     =     ids_tensor((self.batch_size, 3)     ,     vocab_size=2		)

        # append to next input_ids and
        _snake_case		:	int					     =     torch.cat([input_ids, next_tokens]     ,     dim=-1		)
        _snake_case		:	List[Any]					     =     torch.cat([input_mask, next_mask]     ,     dim=-1		)

        _snake_case		:	Any					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     output_hidden_states=lowercase__		)
        _snake_case		:	Dict					     =     output_from_no_past['''hidden_states'''][0]
        _snake_case		:	int					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     past_key_values=lowercase__     ,     output_hidden_states=lowercase__     ,     )['''hidden_states'''][0]

        # select random slice
        _snake_case		:	Union[str, Any]					     =     ids_tensor((1,)     ,     output_from_past.shape[-1]		).item()
        _snake_case		:	str					     =     output_from_no_past[:, -3:, random_slice_idx].detach()
        _snake_case		:	Optional[int]					     =     output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(lowercase__     ,     lowercase__     ,     atol=1E-3		)		)







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






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.prepare_config_and_inputs()
        _snake_case		:	int					     =     config_and_inputs
        _snake_case		:	int					     =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict





@require_torch
class lowerCamelCase						(a__						,      a__						,      a__						,      unittest.TestCase					):
  _lowercase  :	List[Any]       					=				(
      (
          GPTNeoXModel,
          GPTNeoXForCausalLM,
          GPTNeoXForQuestionAnswering,
          GPTNeoXForSequenceClassification,
          GPTNeoXForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  _lowercase  :	Optional[int]       					=				(GPTNeoXForCausalLM,) if is_torch_available() else ()
  _lowercase  :	Union[str, Any]       					=				(
      {
          """feature-extraction""": GPTNeoXModel,
          """question-answering""": GPTNeoXForQuestionAnswering,
          """text-classification""": GPTNeoXForSequenceClassification,
          """text-generation""": GPTNeoXForCausalLM,
          """token-classification""": GPTNeoXForTokenClassification,
          """zero-shot""": GPTNeoXForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _lowercase  :	List[Any]       					=				False
  _lowercase  :	Optional[Any]       					=				False
  _lowercase  :	Optional[int]       					=				False
  _lowercase  :	Union[str, Any]       					=				False







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






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     GPTNeoXModelTester(self		)
        _snake_case		:	Any					     =     ConfigTester(self     ,     config_class=lowercase__     ,     hidden_size=64     ,     num_attention_heads=8		)







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        self.config_tester.run_common_tests()







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Any					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(lowercase__     ,     lowercase__     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(lowercase__     ,     lowercase__     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     self.model_tester.prepare_config_and_inputs_for_decoder()

        _snake_case		:	Dict					     =     None

        self.model_tester.create_and_check_model_as_decoder(lowercase__     ,     lowercase__     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase__     ,     lowercase__     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	str					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	str					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase__		)







  @unittest.skip(reason='''Feed forward chunking is not implemented'''		)
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        pass







  @parameterized.expand([('''linear''',), ('''dynamic''',)]		)
  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	Any					     =     self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		:	List[str]					     =     ids_tensor([1, 10]     ,     config.vocab_size		)
        _snake_case		:	List[str]					     =     ids_tensor([1, int(config.max_position_embeddings * 1.5		)]     ,     config.vocab_size		)

        set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
        _snake_case		:	Tuple					     =     GPTNeoXModel(lowercase__		)
        original_model.to(lowercase__		)
        original_model.eval()
        _snake_case		:	Tuple					     =     original_model(lowercase__		).last_hidden_state
        _snake_case		:	Any					     =     original_model(lowercase__		).last_hidden_state

        set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
        _snake_case		:	str					     =     {'''type''': scaling_type, '''factor''': 10.0}
        _snake_case		:	int					     =     GPTNeoXModel(lowercase__		)
        scaled_model.to(lowercase__		)
        scaled_model.eval()
        _snake_case		:	List[str]					     =     scaled_model(lowercase__		).last_hidden_state
        _snake_case		:	Dict					     =     scaled_model(lowercase__		).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
              self.assertTrue(torch.allclose(lowercase__     ,     lowercase__     ,     atol=1E-5		)		)
        else:
              self.assertFalse(torch.allclose(lowercase__     ,     lowercase__     ,     atol=1E-5		)		)

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(lowercase__     ,     lowercase__     ,     atol=1E-5		)		)





@require_torch
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''		)
        for checkpointing in [True, False]:
              _snake_case		:	Tuple					     =     GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''		)

              if checkpointing:
                    model.gradient_checkpointing_enable()
              else:
                    model.gradient_checkpointing_disable()
              model.to(lowercase__		)

              _snake_case		:	List[Any]					     =     tokenizer('''My favorite food is'''     ,     return_tensors='''pt'''		).to(lowercase__		)
              # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
              # See: https://github.com/huggingface/transformers/pull/24193
              _snake_case		:	Union[str, Any]					     =     '''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

              _snake_case		:	Optional[Any]					     =     model.generate(**lowercase__     ,     do_sample=lowercase__     ,     max_new_tokens=20		)
              _snake_case		:	List[Any]					     =     tokenizer.batch_decode(lowercase__		)[0]

              self.assertEqual(lowercase__     ,     lowercase__		)









'''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 lowerCamelCase						:
  _lowercase  :	Any       					=				LEDConfig
  _lowercase  :	Any       					=				{}
  _lowercase  :	Optional[Any]       					=				"""gelu"""







  def __init__(		self     ,     lowercase__     ,     lowercase__=13     ,     lowercase__=7     ,     lowercase__=True     ,     lowercase__=False     ,     lowercase__=99     ,     lowercase__=32     ,     lowercase__=2     ,     lowercase__=4     ,     lowercase__=37     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=20     ,     lowercase__=2     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=4     ,     )		->						Any:






        """simple docstring"""





        _snake_case		:	Dict					     =     parent
        _snake_case		:	Any					     =     batch_size
        _snake_case		:	List[str]					     =     seq_length
        _snake_case		:	Union[str, Any]					     =     is_training
        _snake_case		:	Tuple					     =     use_labels
        _snake_case		:	int					     =     vocab_size
        _snake_case		:	str					     =     hidden_size
        _snake_case		:	Optional[Any]					     =     num_hidden_layers
        _snake_case		:	List[Any]					     =     num_attention_heads
        _snake_case		:	Optional[int]					     =     intermediate_size

        _snake_case		:	List[Any]					     =     hidden_dropout_prob
        _snake_case		:	List[str]					     =     attention_probs_dropout_prob
        _snake_case		:	Optional[int]					     =     max_position_embeddings
        _snake_case		:	Any					     =     eos_token_id
        _snake_case		:	List[Any]					     =     pad_token_id
        _snake_case		:	Optional[int]					     =     bos_token_id
        _snake_case		:	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
        _snake_case		:	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
        _snake_case		:	Tuple					     =     (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





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

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

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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     TFLEDModel(config=lowercase__		).get_decoder()
        _snake_case		:	Union[str, Any]					     =     inputs_dict['''input_ids''']

        _snake_case		:	List[str]					     =     input_ids[:1, :]
        _snake_case		:	Tuple					     =     inputs_dict['''attention_mask'''][:1, :]
        _snake_case		:	Dict					     =     1

        # first forward pass
        _snake_case		:	Optional[int]					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     use_cache=lowercase__		)

        _snake_case     ,							_snake_case		:	Dict					     =     outputs.to_tuple()

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

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

        _snake_case		:	List[Any]					     =     model(lowercase__     ,     attention_mask=lowercase__		)[0]
        _snake_case		:	Tuple					     =     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
        _snake_case		:	Tuple					     =     int(ids_tensor((1,)     ,     output_from_past.shape[-1]		)		)
        _snake_case		:	int					     =     output_from_no_past[:, -3:, random_slice_idx]
        _snake_case		:	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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      ):





      """simple docstring"""



      if attention_mask is None:
            _snake_case		:	Union[str, Any]					     =     tf.cast(tf.math.not_equal(lowerCAmelCase_			,      config.pad_token_id	)			,      tf.inta	)
      if decoder_attention_mask is None:
            _snake_case		:	str					     =     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		:	int					     =     tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
      if decoder_head_mask is None:
            _snake_case		:	List[str]					     =     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 lowerCamelCase						(a__						,      a__						,      unittest.TestCase					):
  _lowercase  :	Optional[int]       					=				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  _lowercase  :	int       					=				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
  _lowercase  :	Dict       					=				(
      {
          """conversational""": TFLEDForConditionalGeneration,
          """feature-extraction""": TFLEDModel,
          """summarization""": TFLEDForConditionalGeneration,
          """text2text-generation""": TFLEDForConditionalGeneration,
          """translation""": TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  _lowercase  :	int       					=				True
  _lowercase  :	List[Any]       					=				False
  _lowercase  :	str       					=				False
  _lowercase  :	Union[str, Any]       					=				False







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






        """simple docstring"""





        _snake_case		:	str					     =     TFLEDModelTester(self		)
        _snake_case		:	Union[str, Any]					     =     ConfigTester(self     ,     config_class=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        self.config_tester.run_common_tests()







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





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

        _snake_case		:	Dict					     =     True
        _snake_case		:	str					     =     self.model_tester.seq_length
        _snake_case		:	Dict					     =     self.model_tester.encoder_seq_length

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

              if self.is_encoder_decoder:
                    _snake_case		:	Union[str, Any]					     =     model_class(lowercase__		)
                    _snake_case		:	List[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"]
              _snake_case		:	str					     =     True
              _snake_case		:	Tuple					     =     model_class(lowercase__		)
              _snake_case		:	int					     =     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
              _snake_case		:	int					     =     True
              _snake_case		:	List[str]					     =     True
              _snake_case		:	Tuple					     =     model_class(lowercase__		)
              _snake_case		:	Optional[Any]					     =     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 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        pass







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        pass




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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


UpperCAmelCase					:		Dict		=							1E-4





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







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	List[str]					     =     TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		).led

        # change to intended input here
        _snake_case		:	List[str]					     =     _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	Tuple					     =     _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	Tuple					     =     prepare_led_inputs_dict(model.config     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	int					     =     model(**lowercase__		)[0]
        _snake_case		:	Dict					     =     (1, 1_024, 768)
        self.assertEqual(output.shape     ,     lowercase__		)
        # change to expected output here
        _snake_case		:	List[Any]					     =     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 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		)

        # change to intended input here
        _snake_case		:	Dict					     =     _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	Dict					     =     _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	List[str]					     =     prepare_led_inputs_dict(model.config     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	Tuple					     =     model(**lowercase__		)[0]
        _snake_case		:	Any					     =     (1, 1_024, model.config.vocab_size)
        self.assertEqual(output.shape     ,     lowercase__		)
        # change to expected output here
        _snake_case		:	Dict					     =     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		)


