





"""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      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					LEDConfig
  SCREAMING_SNAKE_CASE__							:str	     =					{}
  SCREAMING_SNAKE_CASE__							:List[str]	     =					"gelu"

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

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

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

        _UpperCamelCase      :	Tuple						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

        _UpperCamelCase      :	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					,   )
        _UpperCamelCase      :	Dict						=       prepare_led_inputs_dict(__a					,   __a					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       tf.concat(
            [tf.zeros_like(__a	)[:, :-1], tf.ones_like(__a	)[:, -1:]]					,   axis=-1					,   )
        _UpperCamelCase      :	Union[str, Any]						=       global_attention_mask
        return config, inputs_dict


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[Any]					,   __a   :       int	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       TFLEDModel(config=__a	).get_decoder()
        _UpperCamelCase      :	Tuple						=       inputs_dict["input_ids"]

        _UpperCamelCase      :	int						=       input_ids[:1, :]
        _UpperCamelCase      :	List[str]						=       inputs_dict["attention_mask"][:1, :]
        _UpperCamelCase      :	List[Any]						=       1

        # first forward pass
        _UpperCamelCase      :	Any						=       model(__a					,   attention_mask=__a					,   use_cache=__a	)

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

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

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

        _UpperCamelCase      :	Tuple						=       model(__a					,   attention_mask=__a	)[0]
        _UpperCamelCase      :	int						=       model(__a					,   attention_mask=__a					,   past_key_values=__a	)[0]

        self.parent.assertEqual(next_tokens.shape[1]					,   output_from_past.shape[1]	)

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

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





def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=None		,lowercase_=None		,)					->		Dict:




      """simple docstring"""

      if attention_mask is None:
            _UpperCamelCase      :	str						=       tf.cast(tf.math.not_equal(lowercase_		,config.pad_token_id			)		,tf.inta			)
      if decoder_attention_mask is None:
            _UpperCamelCase      :	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:
            _UpperCamelCase      :	List[str]						=       tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            _UpperCamelCase      :	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      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


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

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				List[Any]:
        _UpperCamelCase      :	int						=       TFLEDModelTester(self	)
        _UpperCamelCase      :	Any						=       ConfigTester(self					,   config_class=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[Any]:
        self.config_tester.run_common_tests()

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				int:
        _UpperCamelCase      :	Tuple						=       self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase      :	Optional[int]						=       tf.zeros_like(inputs_dict["attention_mask"]	)
        _UpperCamelCase      :	Union[str, Any]						=       2
        _UpperCamelCase      :	str						=       tf.where(
            tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices					,   1					,   inputs_dict["global_attention_mask"]					,   )

        _UpperCamelCase      :	Dict						=       True
        _UpperCamelCase      :	str						=       self.model_tester.seq_length
        _UpperCamelCase      :	Union[str, Any]						=       self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(__a   :       Optional[int]	):
              _UpperCamelCase      :	Optional[int]						=       outputs.decoder_attentions
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )

        def check_encoder_attentions_output(__a   :       Optional[Any]	):
              _UpperCamelCase      :	Union[str, Any]						=       [t.numpy() for t in outputs.encoder_attentions]
              _UpperCamelCase      :	List[Any]						=       [t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertListEqual(
                  list(attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )
              self.assertListEqual(
                  list(global_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]					,   )

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	Dict						=       True
              _UpperCamelCase      :	Optional[Any]						=       False
              _UpperCamelCase      :	int						=       False
              _UpperCamelCase      :	Optional[int]						=       model_class(__a	)
              _UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)
              _UpperCamelCase      :	Any						=       len(__a	)
              self.assertEqual(config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

              if self.is_encoder_decoder:
                    _UpperCamelCase      :	Optional[Any]						=       model_class(__a	)
                    _UpperCamelCase      :	List[Any]						=       model(self._prepare_for_class(__a					,   __a	)	)
                    self.assertEqual(config.output_hidden_states					,   __a	)
                    check_decoder_attentions_output(__a	)

              # Check that output attentions can also be changed via the config
              del inputs_dict["output_attentions"]
              _UpperCamelCase      :	int						=       True
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              _UpperCamelCase      :	str						=       model(self._prepare_for_class(__a					,   __a	)	)
              self.assertEqual(config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

              # Check attention is always last and order is fine
              _UpperCamelCase      :	Any						=       True
              _UpperCamelCase      :	List[str]						=       True
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              _UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)

              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)					,   len(__a	)	)
              self.assertEqual(model.config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

  @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        pass


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Tuple:
        # TODO: Head-masking not yet implement
        pass





def 					lowercase__    (					lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

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


lowerCamelCase__				  =   1E-4



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







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Union[str, Any]:
        _UpperCamelCase      :	Any						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

        # change to intended input here
        _UpperCamelCase      :	int						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Tuple						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
        _UpperCamelCase      :	Optional[int]						=       model(**__a	)[0]
        _UpperCamelCase      :	Optional[int]						=       (1, 1024, 768)
        self.assertEqual(output.shape					,   __a	)
        # change to expected output here
        _UpperCamelCase      :	Tuple						=       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]					,   __a					,   atol=1e-3	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

        # change to intended input here
        _UpperCamelCase      :	Optional[int]						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	List[str]						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       model(**__a	)[0]
        _UpperCamelCase      :	int						=       (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape					,   __a	)
        # change to expected output here
        _UpperCamelCase      :	Optional[int]						=       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]					,   __a					,   atol=1e-3					,   rtol=1e-3	)







"""simple docstring"""


from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCamelCase__				  =   TypeVar("KEY")
lowerCamelCase__				  =   TypeVar("VAL")



@dataclass(frozen=_UpperCamelCase		,  slots=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						Generic[KEY, VAL]   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:KEY
  SCREAMING_SNAKE_CASE__							:VAL





class      __SCREAMING_SNAKE_CASE    (						_Item   ):







  '''simple docstring'''


  def __init__(		self   :       List[str]	)		->				None:
        super().__init__(__a					,   __a	)


  def __bool__(		self   :       Dict	)		->				bool:
        return False





lowerCamelCase__				  =   _DeletedItem()



class      __SCREAMING_SNAKE_CASE    (						MutableMapping[KEY, VAL]   ):







  '''simple docstring'''


  def __init__(		self   :       int					,   __a   :       int = 8					,   __a   :       float = 0.75	)		->				None:
        _UpperCamelCase      :	str						=       initial_block_size
        _UpperCamelCase      :	list[_Item | None]						=       [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        _UpperCamelCase      :	List[str]						=       capacity_factor
        _UpperCamelCase      :	Dict						=       0

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       KEY	)		->				int:
        return hash(__a	) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       int	)		->				int:
        return (ind + 1) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				bool:
        _UpperCamelCase      :	List[Any]						=       self._buckets[ind]
        if not stored:
              _UpperCamelCase      :	Tuple						=       _Item(__a					,   __a	)
              self._len += 1
              return True
        elif stored.key == key:
              _UpperCamelCase      :	Union[str, Any]						=       _Item(__a					,   __a	)
              return True
        else:
              return False

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				bool:
        _UpperCamelCase      :	Any						=       len(self._buckets	) * self._capacity_factor
        return len(self	) >= int(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				bool:
        if len(self._buckets	) <= self._initial_block_size:
              return False
        _UpperCamelCase      :	List[str]						=       len(self._buckets	) * self._capacity_factor / 2
        return len(self	) < limit

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       int	)		->				None:
        _UpperCamelCase      :	Any						=       self._buckets
        _UpperCamelCase      :	List[Any]						=       [None] * new_size
        _UpperCamelCase      :	List[str]						=       0
        for item in old_buckets:
              if item:
                    self._add_item(item.key					,   item.val	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				None:
        self._resize(len(self._buckets	) * 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				None:
        self._resize(len(self._buckets	) // 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       KEY	)		->				Iterator[int]:
        _UpperCamelCase      :	str						=       self._get_bucket_index(__a	)
        for _ in range(len(self._buckets	)	):
              yield ind
              _UpperCamelCase      :	Tuple						=       self._get_next_ind(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        for ind in self._iterate_buckets(__a	):
              if self._try_set(__a					,   __a					,   __a	):
                    break

  def __setitem__(		self   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        if self._is_full():
              self._size_up()

        self._add_item(__a					,   __a	)

  def __delitem__(		self   :       str					,   __a   :       KEY	)		->				None:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    raise KeyError(__a	)
              if item is _deleted:
                    continue
              if item.key == key:
                    _UpperCamelCase      :	List[Any]						=       _deleted
                    self._len -= 1
                    break
        if self._is_sparse():
              self._size_down()

  def __getitem__(		self   :       str					,   __a   :       KEY	)		->				VAL:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    break
              if item is _deleted:
                    continue
              if item.key == key:
                    return item.val
        raise KeyError(__a	)

  def __len__(		self   :       List[Any]	)		->				int:
        return self._len

  def __iter__(		self   :       List[str]	)		->				Iterator[KEY]:
        yield from (item.key for item in self._buckets if item)


  def __repr__(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       " ,".join(
            F'''{item.key}: {item.val}''' for item in self._buckets if item	)
        return F'''HashMap({val_string})'''
