





'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
   import torch


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)
UpperCAmelCase					:		Tuple		=							{'vocab_file': 'spiece.model'}

UpperCAmelCase					:		str		=							{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

UpperCAmelCase					:		Any		=							{
    'AI-Sweden/gpt-sw3-126m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-350m': 2_0_4_8,
    'AI-Sweden/gpt-sw3-1.6b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-6.7b': 2_0_4_8,
    'AI-Sweden/gpt-sw3-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Any       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Optional[int]       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[Any]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__     ,     lowercase__=False     ,     lowercase__=False     ,     lowercase__=False     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     {} if sp_model_kwargs is None else sp_model_kwargs

        _snake_case		:	List[str]					     =     kwargs.get('''name_or_path'''		)
        if name_or_path is None:
              logger.warning(
                  '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
                  ''' you are testing the model, this can safely be ignored'''		)
              _snake_case		:	List[str]					     =     '''None'''

        # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
        _snake_case		:	Optional[int]					     =     '''<|endoftext|>''' if eos_token is None else eos_token
        _snake_case		:	Tuple					     =     '''<unk>''' if unk_token is None else unk_token
        if "gpt-sw3-7b" in name_or_path:
              _snake_case		:	str					     =     unk_token if pad_token is None else pad_token
              _snake_case		:	List[Any]					     =     eos_token if bos_token is None else bos_token
        else:
              _snake_case		:	int					     =     '''<pad>''' if pad_token is None else pad_token
              _snake_case		:	List[str]					     =     '''<s>''' if bos_token is None else bos_token

        super().__init__(
            do_lower_case=lowercase__     ,     remove_space=lowercase__     ,     keep_accents=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     unk_token=lowercase__     ,     pad_token=lowercase__     ,     sp_model_kwargs=self.sp_model_kwargs     ,     **lowercase__     ,     )

        _snake_case		:	Union[str, Any]					     =     do_lower_case
        _snake_case		:	Optional[Any]					     =     remove_space
        _snake_case		:	int					     =     keep_accents
        _snake_case		:	Optional[Any]					     =     vocab_file

        _snake_case		:	Optional[int]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowercase__		)

        # Used for whitespace normalization in input texts
        # fmt : off
        _snake_case		:	str					     =     {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
        # fmt : on

        # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
        _snake_case		:	Optional[Any]					     =     re.compile(
            F'''[{"".join(map(lowercase__     ,     list(range(0     ,     9		)		) + list(range(11     ,     32		)		) + list(range(127     ,     160		)		) + [160, 173, 8_203]		)		)}]'''		)







  def __getstate__(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     self.__dict__.copy()
        _snake_case		:	Tuple					     =     None
        return state







  def __setstate__(		self     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     d

        # for backward compatibility
        if not hasattr(self     ,     '''sp_model_kwargs'''		):
              _snake_case		:	int					     =     {}

        _snake_case		:	List[Any]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)







  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        return len(self.sp_model		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self.non_printing_characters_re.sub(''''''     ,     lowercase__		)

        # Normalize whitespaces
        _snake_case		:	str					     =     ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]		)

        # NFC Unicode normalization
        _snake_case		:	str					     =     unicodedata.normalize('''NFC'''     ,     lowercase__		)
        return text







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






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.preprocess_text(lowercase__		)
        return self.sp_model.encode(lowercase__     ,     out_type=lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						int:






        """simple docstring"""





        return self.sp_model.PieceToId(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        return self.sp_model.IdToPiece(lowercase__		)







  @staticmethod
  def 				UpperCAmelCase_				(		lowercase__		)		->						str:






        """simple docstring"""





        return out_string







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        _snake_case		:	Any					     =     ''''''
        _snake_case		:	Union[str, Any]					     =     False
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                    if not prev_is_special:
                          out_string += " "

                    out_string += self.sp_model.decode(lowercase__		) + token
                    _snake_case		:	str					     =     True
                    _snake_case		:	Union[str, Any]					     =     []
              else:
                    current_sub_tokens.append(lowercase__		)
                    _snake_case		:	int					     =     False
        out_string += self.sp_model.decode(lowercase__		)

        return out_string







  def 				UpperCAmelCase_				(		self		)		->						Dict[str, int]:






        """simple docstring"""





        _snake_case		:	Any					     =     {self.convert_ids_to_tokens(lowercase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        if not os.path.isdir(lowercase__		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _snake_case		:	Union[str, Any]					     =     os.path.join(
            lowercase__     ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase__		) and os.path.isfile(self.vocab_file		):
              copyfile(self.vocab_file     ,     lowercase__		)
        elif not os.path.isfile(self.vocab_file		):
              with open(lowercase__     ,     '''wb'''		) as fi:
                    _snake_case		:	Optional[Any]					     =     self.sp_model.serialized_model_proto()
                    fi.write(lowercase__		)

        return (out_vocab_file,)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = False		)		->						Union[List[int], List[List[int]], "torch.Tensor"]:






        """simple docstring"""





        if isinstance(lowercase__     ,     lowercase__		):
              _snake_case		:	Optional[int]					     =     self.preprocess_text(lowercase__		)
              _snake_case		:	Union[str, Any]					     =     self.sp_model.encode(lowercase__		)
        else:
              _snake_case		:	Optional[int]					     =     [self.preprocess_text(lowercase__		) for t in text]
              _snake_case		:	Union[str, Any]					     =     self.sp_model.encode(lowercase__		)

        if return_tensors is True or return_tensors == "pt":
              _snake_case		:	Union[str, Any]					     =     torch.tensor(lowercase__		)

        return token_ids







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        return self.sp_model.decode(lowercase__		)







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






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
        _snake_case		:	Union[str, Any]					     =     (
            F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(lowercase__		) + F'''{self.bos_token}Bot:'''
        )
        return self.encode(text=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		)


