import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import jax
   import jaxlib

__lowerCamelCase      :			Optional[int]							       =		get_logger()

__lowerCamelCase      :			Optional[dict]							       =		None







class __snake_case    (						TensorFormatter[Mapping, "jax.Array", Mapping]		):




   def __init__(   self      :    List[str]	,     _lowercase      :    Any=None	,     _lowercase      :    Any=None	,     **_lowercase      :    List[str]				):



     """simple docstring"""






     super().__init__(features=lowerCAmelCase__				)
     import jax
     from jaxlib.xla_client import Device

     if isinstance(lowerCAmelCase__	,     lowerCAmelCase__				):
       raise ValueError(
           f"""Expected {device} to be a `str` not {type(lowerCAmelCase__				)}, as `jaxlib.xla_extension.Device` """
           """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
           """the device with `str()` to get its string identifier that will be internally mapped """
           """to the actual `jaxlib.xla_extension.Device`."""				)
     SCREAMING_SNAKE_CASE__      							=				device if isinstance(lowerCAmelCase__	,     lowerCAmelCase__				) else str(jax.devices()[0]				)
     # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
     # with `pickle` nor with `dill`, so we need to use a global variable instead
     global DEVICE_MAPPING
     if DEVICE_MAPPING is None:
       SCREAMING_SNAKE_CASE__      							=				self._map_devices_to_str()
     if self.device not in list(DEVICE_MAPPING.keys()				):
       logger.warning(
           f"""Device with string identifier {self.device} not listed among the available """
           f"""devices: {list(DEVICE_MAPPING.keys()				)}, so falling back to the default """
           f"""device: {str(jax.devices()[0]				)}."""				)
       SCREAMING_SNAKE_CASE__      							=				str(jax.devices()[0]				)
     SCREAMING_SNAKE_CASE__      							=				jnp_array_kwargs




   @staticmethod
   def      __a						(   ):



     """simple docstring"""






     import jax

     return {str(lowerCAmelCase__				): device for device in jax.devices()}




   def      __a						(   self      :    int	,     _lowercase      :    Tuple				):



     """simple docstring"""






     import jax
     import jax.numpy as jnp

     if isinstance(lowerCAmelCase__	,     lowerCAmelCase__				) and column:
       if all(
           isinstance(lowerCAmelCase__	,     jax.Array				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
         return jnp.stack(lowerCAmelCase__	,     axis=0				)
     return column




   def      __a						(   self      :    Any	,     _lowercase      :    Union[str, Any]				):



     """simple docstring"""






     import jax
     import jax.numpy as jnp

     if isinstance(lowerCAmelCase__	,     (str, bytes, type(lowerCAmelCase__				))				):
       return value
     elif isinstance(lowerCAmelCase__	,     (np.character, np.ndarray)				) and np.issubdtype(value.dtype	,     np.character				):
       return value.tolist()

     SCREAMING_SNAKE_CASE__      							=				{}

     if isinstance(lowerCAmelCase__	,     (np.number, np.ndarray)				) and np.issubdtype(value.dtype	,     np.integer				):
       # the default int precision depends on the jax config
       # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
       if jax.config.jax_enable_xaa:
         SCREAMING_SNAKE_CASE__      							=				{"dtype": jnp.intaa}
       else:
         SCREAMING_SNAKE_CASE__      							=				{"dtype": jnp.intaa}
     elif isinstance(lowerCAmelCase__	,     (np.number, np.ndarray)				) and np.issubdtype(value.dtype	,     np.floating				):
       SCREAMING_SNAKE_CASE__      							=				{"dtype": jnp.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
       import PIL.Image

       if isinstance(lowerCAmelCase__	,     PIL.Image.Image				):
         SCREAMING_SNAKE_CASE__      							=				np.asarray(lowerCAmelCase__				)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
     global DEVICE_MAPPING
     if DEVICE_MAPPING is None:
       SCREAMING_SNAKE_CASE__      							=				self._map_devices_to_str()

     with jax.default_device(DEVICE_MAPPING[self.device]				):
       # calling jnp.array on a np.ndarray does copy the data
       # see https://github.com/google/jax/issues/4486
       return jnp.array(lowerCAmelCase__	,     **{**default_dtype, **self.jnp_array_kwargs}				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     import jax

     # support for torch, tf, jax etc.
     if config.TORCH_AVAILABLE and "torch" in sys.modules:
       import torch

       if isinstance(lowerCAmelCase__	,     torch.Tensor				):
         return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
     if hasattr(lowerCAmelCase__	,     """__array__"""				) and not isinstance(lowerCAmelCase__	,     jax.Array				):
       SCREAMING_SNAKE_CASE__      							=				data_struct.__array__()
     # support for nested types like struct of list of struct
     if isinstance(lowerCAmelCase__	,     np.ndarray				):
       if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
         return self._consolidate([self.recursive_tensorize(lowerCAmelCase__				) for substruct in data_struct]				)
     elif isinstance(lowerCAmelCase__	,     (list, tuple)				):
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__				) for substruct in data_struct]				)
     return self._tensorize(lowerCAmelCase__				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Tuple				):



     """simple docstring"""






     return map_nested(self._recursive_tensorize	,     lowerCAmelCase__	,     map_list=lowerCAmelCase__				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.numpy_arrow_extractor().extract_row(lowerCAmelCase__				)
     SCREAMING_SNAKE_CASE__      							=				self.python_features_decoder.decode_row(lowerCAmelCase__				)
     return self.recursive_tensorize(lowerCAmelCase__				)




   def      __a						(   self      :    Dict	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.numpy_arrow_extractor().extract_column(lowerCAmelCase__				)
     SCREAMING_SNAKE_CASE__      							=				self.python_features_decoder.decode_column(lowerCAmelCase__	,     pa_table.column_names[0]				)
     SCREAMING_SNAKE_CASE__      							=				self.recursive_tensorize(lowerCAmelCase__				)
     SCREAMING_SNAKE_CASE__      							=				self._consolidate(lowerCAmelCase__				)
     return column




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__				)
     SCREAMING_SNAKE_CASE__      							=				self.python_features_decoder.decode_batch(lowerCAmelCase__				)
     SCREAMING_SNAKE_CASE__      							=				self.recursive_tensorize(lowerCAmelCase__				)
     for column_name in batch:
       SCREAMING_SNAKE_CASE__      							=				self._consolidate(batch[column_name]				)
     return batch

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 __snake_case    :
   lowerCAmelCase_														=							LEDConfig
   lowerCAmelCase_														=							{}
   lowerCAmelCase_														=							"gelu"




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				seq_length
     SCREAMING_SNAKE_CASE__      							=				is_training
     SCREAMING_SNAKE_CASE__      							=				use_labels
     SCREAMING_SNAKE_CASE__      							=				vocab_size
     SCREAMING_SNAKE_CASE__      							=				hidden_size
     SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
     SCREAMING_SNAKE_CASE__      							=				num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				intermediate_size

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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ids_tensor([self.batch_size, self.seq_length - 1]	,     self.vocab_size				)
     SCREAMING_SNAKE_CASE__      							=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)	,     1				)
     SCREAMING_SNAKE_CASE__      							=				tf.concat([input_ids, eos_tensor]	,     axis=1				)

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

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




   def      __a						(   self      :    Tuple	,     _lowercase      :    int	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFLEDModel(config=_lowercase				).get_decoder()
     SCREAMING_SNAKE_CASE__      							=				inputs_dict["""input_ids"""]

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

     # first forward pass
     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     attention_mask=_lowercase	,     use_cache=_lowercase				)

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				outputs.to_tuple()

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

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

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

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





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	List[str]=None			,					__UpperCamelCase   :	Union[str, Any]=None			,					__UpperCamelCase   :	Dict=None			,					__UpperCamelCase   :	Optional[Any]=None			,					)							-> int:







  """simple docstring"""





  if attention_mask is None:
    SCREAMING_SNAKE_CASE__      							=				tf.cast(tf.math.not_equal(__UpperCamelCase			,					config.pad_token_id     )			,					tf.inta     )
  if decoder_attention_mask is None:
    SCREAMING_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:
    SCREAMING_SNAKE_CASE__      							=				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    SCREAMING_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 __snake_case    (						lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
   lowerCAmelCase_														=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
   lowerCAmelCase_														=							(
       {
           "conversational": TFLEDForConditionalGeneration,
           "feature-extraction": TFLEDModel,
           "summarization": TFLEDForConditionalGeneration,
           "text2text-generation": TFLEDForConditionalGeneration,
           "translation": TFLEDForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFLEDModelTester(self				)
     SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_lowercase				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__      							=				tf.zeros_like(inputs_dict["""attention_mask"""]				)
     SCREAMING_SNAKE_CASE__      							=				2
     SCREAMING_SNAKE_CASE__      							=				tf.where(
         tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices	,     1	,     inputs_dict["""global_attention_mask"""]	,     )

     SCREAMING_SNAKE_CASE__      							=				True
     SCREAMING_SNAKE_CASE__      							=				self.model_tester.seq_length
     SCREAMING_SNAKE_CASE__      							=				self.model_tester.encoder_seq_length

     def check_decoder_attentions_output(_lowercase      :    List[str]				):
       SCREAMING_SNAKE_CASE__      							=				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      :    List[str]				):
       SCREAMING_SNAKE_CASE__      							=				[t.numpy() for t in outputs.encoder_attentions]
       SCREAMING_SNAKE_CASE__      							=				[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:
       SCREAMING_SNAKE_CASE__      							=				True
       SCREAMING_SNAKE_CASE__      							=				False
       SCREAMING_SNAKE_CASE__      							=				False
       SCREAMING_SNAKE_CASE__      							=				model_class(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				model(self._prepare_for_class(_lowercase	,     _lowercase				)				)
       SCREAMING_SNAKE_CASE__      							=				len(_lowercase				)
       self.assertEqual(config.output_hidden_states	,     _lowercase				)
       check_encoder_attentions_output(_lowercase				)

       if self.is_encoder_decoder:
         SCREAMING_SNAKE_CASE__      							=				model_class(_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				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"]
       SCREAMING_SNAKE_CASE__      							=				True
       SCREAMING_SNAKE_CASE__      							=				model_class(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				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
       SCREAMING_SNAKE_CASE__      							=				True
       SCREAMING_SNAKE_CASE__      							=				True
       SCREAMING_SNAKE_CASE__      							=				model_class(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[Any]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> Dict:







  """simple docstring"""





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


__lowerCamelCase      :			List[str]							       =		1e-4







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




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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

     # change to intended input here
     SCREAMING_SNAKE_CASE__      							=				_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
     SCREAMING_SNAKE_CASE__      							=				_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
     SCREAMING_SNAKE_CASE__      							=				prepare_led_inputs_dict(model.config	,     _lowercase	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				model(**_lowercase				)[0]
     SCREAMING_SNAKE_CASE__      							=				(1, 10_24, 7_68)
     self.assertEqual(output.shape	,     _lowercase				)
     # change to expected output here
     SCREAMING_SNAKE_CASE__      							=				tf.convert_to_tensor(
         [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]	,     )
     tf.debugging.assert_near(output[:, :3, :3]	,     _lowercase	,     atol=1E-3				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






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

     # change to intended input here
     SCREAMING_SNAKE_CASE__      							=				_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
     SCREAMING_SNAKE_CASE__      							=				_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]				)
     SCREAMING_SNAKE_CASE__      							=				prepare_led_inputs_dict(model.config	,     _lowercase	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				model(**_lowercase				)[0]
     SCREAMING_SNAKE_CASE__      							=				(1, 10_24, model.config.vocab_size)
     self.assertEqual(output.shape	,     _lowercase				)
     # change to expected output here
     SCREAMING_SNAKE_CASE__      							=				tf.convert_to_tensor(
         [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]	,     )
     tf.debugging.assert_near(output[:, :3, :3]	,     _lowercase	,     atol=1E-3	,     rtol=1E-3				)
