"""simple docstring"""






import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A			   = getLogger(__name__)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = 8       ,    __UpperCAmelCase = 1_0_2_4       ,    __UpperCAmelCase="val"       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=False       ,    __UpperCAmelCase="summarization"       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=1       ,    __UpperCAmelCase = None       ,    __UpperCAmelCase=""       ,    **__UpperCAmelCase       ,    )  ->  Any:
   lowercase__:   List[Any]					=      str(lowerCAmelCase__ )
   assert local_rank is not None
   torch.distributed.init_process_group(backend='''nccl'''       ,    rank=lowerCAmelCase__ )

   lowercase__:   Tuple					=      Path(lowerCAmelCase__ )
   lowercase__:   Dict					=      save_dir.joinpath(F"""rank_{local_rank}_output.json""" )
   torch.cuda.set_device(lowerCAmelCase__ )
   lowercase__:   Dict					=      AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ ).cuda()
   if fpaa:
      lowercase__:   str					=      model.half()
   # determine if we need to increase num_beams
   use_task_specific_params(lowerCAmelCase__       ,    lowerCAmelCase__ )  # update config with task specific params
   lowercase__:   List[str]					=      generate_kwargs.pop('''num_beams'''       ,    model.config.num_beams )  # AttributeError risk?
   if num_return_sequences > num_beams:
      lowercase__:   Dict					=      num_return_sequences

   lowercase__:   Optional[Any]					=      AutoTokenizer.from_pretrained(lowerCAmelCase__ )
   logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

   if max_source_length is None:
      lowercase__:   Dict					=      tokenizer.model_max_length
   if prefix is None:
      lowercase__:   Dict					=      prefix or getattr(model.config       ,    '''prefix'''       ,    '''''' ) or ''''''
   lowercase__:   List[Any]					=      SeqaSeqDataset(
       lowerCAmelCase__       ,    lowerCAmelCase__       ,    lowerCAmelCase__       ,    max_target_length=1_0_2_4       ,    type_path=lowerCAmelCase__       ,    n_obs=lowerCAmelCase__       ,    prefix=lowerCAmelCase__       ,    **lowerCAmelCase__       ,    )
   # I set shuffle=True for a more accurate progress bar.
   # If all the longest samples are first, the prog bar estimate is too high at the beginning.
   lowercase__:   Tuple					=      ds.make_sortish_sampler(lowerCAmelCase__       ,    distributed=lowerCAmelCase__       ,    add_extra_examples=lowerCAmelCase__       ,    shuffle=lowerCAmelCase__ )
   lowercase__:   List[Any]					=      DataLoader(lowerCAmelCase__       ,    sampler=lowerCAmelCase__       ,    batch_size=lowerCAmelCase__       ,    collate_fn=ds.collate_fn )
   lowercase__:   List[Any]					=      []
   for batch in tqdm(lowerCAmelCase__ ):
      lowercase__:   Any					=      model.generate(
          input_ids=batch['''input_ids'''].to(model.device )       ,    attention_mask=batch['''attention_mask'''].to(model.device )       ,    num_return_sequences=lowerCAmelCase__       ,    num_beams=lowerCAmelCase__       ,    **lowerCAmelCase__       ,    )
      lowercase__:   List[Any]					=      tokenizer.batch_decode(lowerCAmelCase__       ,    skip_special_tokens=lowerCAmelCase__       ,    clean_up_tokenization_spaces=lowerCAmelCase__ )
      lowercase__:   int					=      batch['''ids''']
      if num_return_sequences > 1:
         lowercase__:   List[str]					=      chunks(lowerCAmelCase__       ,    lowerCAmelCase__ )  # batch size chunks, each of size num_return_seq
      for i, pred in enumerate(lowerCAmelCase__ ):
         results.append({'''pred''': pred, '''id''': ids[i].item()} )
   save_json(lowerCAmelCase__       ,    lowerCAmelCase__ )
   return results, sampler.num_replicas



def SCREAMING_SNAKE_CASE__			(   )  ->  List[str]:
   lowercase__:   str					=      argparse.ArgumentParser(
       epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate''' )
   parser.add_argument('''--data_dir'''       ,    type=lowerCAmelCase__       ,    help='''like cnn_dm/test.source''' )
   parser.add_argument(
       '''--model_name'''       ,    type=lowerCAmelCase__       ,    help='''like facebook/bart-large-cnn,t5-base, etc.'''       ,    default='''sshleifer/distilbart-xsum-12-3'''       ,    )
   parser.add_argument('''--save_dir'''       ,    type=lowerCAmelCase__       ,    help='''where to save'''       ,    default='''tmp_gen''' )
   parser.add_argument('''--max_source_length'''       ,    type=lowerCAmelCase__       ,    default=lowerCAmelCase__ )
   parser.add_argument(
       '''--type_path'''       ,    type=lowerCAmelCase__       ,    default='''test'''       ,    help='''which subset to evaluate typically train/val/test''' )
   parser.add_argument('''--task'''       ,    type=lowerCAmelCase__       ,    default='''summarization'''       ,    help='''used for task_specific_params + metrics''' )
   parser.add_argument('''--bs'''       ,    type=lowerCAmelCase__       ,    default=8       ,    required=lowerCAmelCase__       ,    help='''batch size''' )
   parser.add_argument(
       '''--local_rank'''       ,    type=lowerCAmelCase__       ,    default=-1       ,    required=lowerCAmelCase__       ,    help='''should be passed by distributed.launch''' )

   parser.add_argument(
       '''--n_obs'''       ,    type=lowerCAmelCase__       ,    default=lowerCAmelCase__       ,    required=lowerCAmelCase__       ,    help='''How many observations. Defaults to all.''' )
   parser.add_argument(
       '''--num_return_sequences'''       ,    type=lowerCAmelCase__       ,    default=1       ,    required=lowerCAmelCase__       ,    help='''How many sequences to return''' )
   parser.add_argument(
       '''--sync_timeout'''       ,    type=lowerCAmelCase__       ,    default=6_0_0       ,    required=lowerCAmelCase__       ,    help='''How long should master process wait for other processes to finish.'''       ,    )
   parser.add_argument('''--src_lang'''       ,    type=lowerCAmelCase__       ,    default=lowerCAmelCase__       ,    required=lowerCAmelCase__ )
   parser.add_argument('''--tgt_lang'''       ,    type=lowerCAmelCase__       ,    default=lowerCAmelCase__       ,    required=lowerCAmelCase__ )
   parser.add_argument(
       '''--prefix'''       ,    type=lowerCAmelCase__       ,    required=lowerCAmelCase__       ,    default=lowerCAmelCase__       ,    help='''will be added to the begininng of src examples''' )
   parser.add_argument('''--fp16'''       ,    action='''store_true''' )
   parser.add_argument('''--debug'''       ,    action='''store_true''' )
   lowercase__:   int					=      time.time()
   lowercase__:   Tuple					=      parser.parse_known_args()
   lowercase__:   Optional[int]					=      parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__ )
   if generate_kwargs and args.local_rank <= 0:
      print(F"""parsed the following generate kwargs: {generate_kwargs}""" )
   lowercase__:   Union[str, Any]					=      Path(args.save_dir + '''_tmp''' )
   Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )  # this handles locking.
   lowercase__:   Dict					=      list(json_save_dir.glob('''rank_*.json''' ) )
   if intermediate_files:
      raise ValueError(F"""Found files at {json_save_dir} please move or remove them.""" )
      # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
   lowercase__:   Optional[Any]					=      {}
   if args.src_lang is not None:
      lowercase__:   int					=      args.src_lang
   if args.tgt_lang is not None:
      lowercase__:   Optional[Any]					=      args.tgt_lang

   Path(args.save_dir ).mkdir(exist_ok=lowerCAmelCase__ )
   lowercase__:   Tuple					=      eval_data_dir(
       args.data_dir       ,    lowerCAmelCase__       ,    args.model_name       ,    type_path=args.type_path       ,    bs=args.bs       ,    fpaa=args.fpaa       ,    task=args.task       ,    local_rank=args.local_rank       ,    n_obs=args.n_obs       ,    max_source_length=args.max_source_length       ,    num_return_sequences=args.num_return_sequences       ,    prefix=args.prefix       ,    dataset_kwargs=lowerCAmelCase__       ,    **lowerCAmelCase__       ,    )

   if args.local_rank <= 0:
      lowercase__:   int					=      Path(args.save_dir )
      save_dir.mkdir(exist_ok=lowerCAmelCase__ )
      lowercase__:   List[str]					=      gather_results_from_each_node(lowerCAmelCase__       ,    lowerCAmelCase__       ,    args.sync_timeout )
      lowercase__:   int					=      combine_partial_results(lowerCAmelCase__ )
      if args.num_return_sequences > 1:
         lowercase__:   List[Any]					=      save_dir.joinpath('''pseudolabel_results.json''' )
         print(F"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/""" )
         save_json(lowerCAmelCase__       ,    lowerCAmelCase__ )
         return
      lowercase__:   Any					=      Path(args.data_dir ).joinpath(args.type_path + '''.target''' )
      with open(lowerCAmelCase__ ) as f:
         lowercase__:   Optional[int]					=      [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase__ )]

      # Calculate metrics, save metrics,  and save _generations.txt
      lowercase__:   str					=      '''translation''' in args.task
      lowercase__:   List[str]					=      calculate_bleu if calc_bleu else calculate_rouge
      lowercase__:   Any					=      '''bleu''' if calc_bleu else '''rouge'''
      lowercase__:   Dict					=      score_fn(lowerCAmelCase__       ,    lowerCAmelCase__ )
      lowercase__:   Dict					=      len(lowerCAmelCase__ )
      lowercase__:   str					=      time.time() - start_time
      lowercase__:   List[str]					=      round(runtime / metrics['''n_obs''']       ,    4 )
      lowercase__:   Tuple					=      num_replicas
      # TODO(@stas00): add whatever metadata to metrics
      lowercase__:   Optional[int]					=      save_dir.joinpath(F"""{args.type_path}_{metric_name}.json""" )
      save_json(lowerCAmelCase__       ,    lowerCAmelCase__       ,    indent=lowerCAmelCase__ )
      print(lowerCAmelCase__ )
      write_txt_file(lowerCAmelCase__       ,    save_dir.joinpath(F"""{args.type_path}_generations.txt""" ) )
      if args.debug:
         write_txt_file(lowerCAmelCase__       ,    save_dir.joinpath(F"""{args.type_path}.target""" ) )
      else:
         shutil.rmtree(lowerCAmelCase__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[int]					=      []
   for partial_result in partial_results:
      records.extend(lowerCAmelCase__ )
   lowercase__:   Tuple					=      sorted(lowerCAmelCase__       ,    key=lambda __UpperCAmelCase : x["id"] )
   lowercase__:   Tuple					=      [x['''pred'''] for x in records]
   return preds



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   # WAIT FOR lots of .json files
   lowercase__:   Tuple					=      time.time()
   logger.info('''waiting for all nodes to finish''' )
   lowercase__:   Optional[int]					=      None
   while (time.time() - start_wait) < timeout:
      lowercase__:   Optional[int]					=      list(save_dir.glob('''rank_*.json''' ) )
      if len(lowerCAmelCase__ ) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         lowercase__:   Tuple					=      lmap(lowerCAmelCase__       ,    lowerCAmelCase__ )
         return json_data
      except JSONDecodeError:
         continue
   else:
      raise TimeoutError('''Rank 0 gave up on waiting for other processes''' )
   # Unreachable


if __name__ == "__main__":
       # Usage for MT:
       run_generate()


from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      LEDConfig
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      {}
   _SCREAMING_SNAKE_CASE      :							int	       =      "gelu"
   def __init__(self :					List[str]					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Union[str, Any]=1_3					,				snake_case_ :					Optional[Any]=7					,				snake_case_ :					Any=True					,				snake_case_ :					List[Any]=False					,				snake_case_ :					str=9_9					,				snake_case_ :					Any=3_2					,				snake_case_ :					Dict=2					,				snake_case_ :					List[Any]=4					,				snake_case_ :					Optional[int]=3_7					,				snake_case_ :					Dict=0.1					,				snake_case_ :					int=0.1					,				snake_case_ :					Optional[Any]=2_0					,				snake_case_ :					Optional[Any]=2					,				snake_case_ :					Optional[int]=1					,				snake_case_ :					Optional[int]=0					,				snake_case_ :					str=4					,				):
    __a :  List[Any]						=  parent
    __a :  Union[str, Any]						=  batch_size
    __a :  List[str]						=  seq_length
    __a :  Any						=  is_training
    __a :  Tuple						=  use_labels
    __a :  List[Any]						=  vocab_size
    __a :  Optional[Any]						=  hidden_size
    __a :  int						=  num_hidden_layers
    __a :  Optional[int]						=  num_attention_heads
    __a :  int						=  intermediate_size

    __a :  Union[str, Any]						=  hidden_dropout_prob
    __a :  Dict						=  attention_probs_dropout_prob
    __a :  int						=  max_position_embeddings
    __a :  Tuple						=  eos_token_id
    __a :  Optional[Any]						=  pad_token_id
    __a :  List[str]						=  bos_token_id
    __a :  List[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
    __a :  Union[str, 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
    __a :  List[str]						=  (
        self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
    )
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Dict						=  ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size   )
    __a :  Union[str, Any]						=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )					,				1   )
    __a :  int						=  tf.concat([input_ids, eos_tensor]					,				axis=1   )

    __a :  List[str]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

    __a :  Optional[int]						=  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					,				)
    __a :  Optional[int]						=  prepare_led_inputs_dict(snake_case_					,				snake_case_					,				snake_case_   )
    __a :  Dict						=  tf.concat(
        [tf.zeros_like(snake_case_   )[:, :-1], tf.ones_like(snake_case_   )[:, -1:]]					,				axis=-1					,				)
    __a :  Tuple						=  global_attention_mask
    return config, inputs_dict







   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Dict					,				snake_case_ :					int   ):
    __a :  List[str]						=  TFLEDModel(config=snake_case_   ).get_decoder()
    __a :  Dict						=  inputs_dict['''input_ids''']

    __a :  Dict						=  input_ids[:1, :]
    __a :  Any						=  inputs_dict['''attention_mask'''][:1, :]
    __a :  List[str]						=  1

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

    __a				,       __a :  Tuple						=  outputs.to_tuple()

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

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

    __a :  Optional[int]						=  model(snake_case_					,				attention_mask=snake_case_   )[0]
    __a :  int						=  model(snake_case_					,				attention_mask=snake_case_					,				past_key_values=snake_case_   )[0]

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

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

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





def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				List[Any]=None  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Union[str, Any]=None  ,   lowerCAmelCase__							:				List[str]=None  ,   ):
 if attention_mask is None:
  __a :  Any						=  tf.cast(tf.math.not_equal(lowerCAmelCase__  ,   config.pad_token_id  )  ,   tf.inta  )
 if decoder_attention_mask is None:
  __a :  Dict						=  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:
  __a :  Dict						=  tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
 if decoder_head_mask is None:
  __a :  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 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Dict	       =      (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE      :							Tuple	       =      (TFLEDForConditionalGeneration,) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      (
       {
           "conversational": TFLEDForConditionalGeneration,
           "feature-extraction": TFLEDModel,
           "summarization": TFLEDForConditionalGeneration,
           "text2text-generation": TFLEDForConditionalGeneration,
           "translation": TFLEDForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      True
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      False
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      False
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      False
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  List[str]						=  TFLEDModelTester(self   )
    __a :  Optional[int]						=  ConfigTester(self					,				config_class=snake_case_   )
   def       lowerCAmelCase			(self :					Any   ):
    self.config_tester.run_common_tests()
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Union[str, Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*snake_case_   )
   def       lowerCAmelCase			(self :					Any   ):
    __a				,       __a :  Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
    __a :  Any						=  tf.zeros_like(inputs_dict['''attention_mask''']   )
    __a :  Tuple						=  2
    __a :  Dict						=  tf.where(
        tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices					,				1					,				inputs_dict['''global_attention_mask''']					,				)

    __a :  List[str]						=  True
    __a :  Tuple						=  self.model_tester.seq_length
    __a :  Any						=  self.model_tester.encoder_seq_length

    def check_decoder_attentions_output(snake_case_ :					Any   ):
     __a :  str						=  outputs.decoder_attentions
     self.assertEqual(len(snake_case_   )					,				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(snake_case_ :					Optional[int]   ):
     __a :  int						=  [t.numpy() for t in outputs.encoder_attentions]
     __a :  int						=  [t.numpy() for t in outputs.encoder_global_attentions]
     self.assertEqual(len(snake_case_   )					,				self.model_tester.num_hidden_layers   )
     self.assertEqual(len(snake_case_   )					,				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:
     __a :  Dict						=  True
     __a :  Optional[Any]						=  False
     __a :  List[str]						=  False
     __a :  List[Any]						=  model_class(snake_case_   )
     __a :  List[str]						=  model(self._prepare_for_class(snake_case_					,				snake_case_   )   )
     __a :  List[str]						=  len(snake_case_   )
     self.assertEqual(config.output_hidden_states					,				snake_case_   )
     check_encoder_attentions_output(snake_case_   )

     if self.is_encoder_decoder:
      __a :  List[str]						=  model_class(snake_case_   )
      __a :  int						=  model(self._prepare_for_class(snake_case_					,				snake_case_   )   )
      self.assertEqual(config.output_hidden_states					,				snake_case_   )
      check_decoder_attentions_output(snake_case_   )

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

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

     self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)					,				len(snake_case_   )   )
     self.assertEqual(model.config.output_hidden_states					,				snake_case_   )
     check_encoder_attentions_output(snake_case_   )
   @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
   def       lowerCAmelCase			(self :					List[str]   ):
    pass







   def       lowerCAmelCase			(self :					List[Any]   ):
    # TODO: Head-masking not yet implement
    pass





def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ):
 return tf.constant(lowerCAmelCase__  ,   dtype=tf.intaa  )


lowercase__       =1e-4



@slow
@require_tf
class 		UpperCamelCase__  (       unittest.TestCase    ):
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Dict						=  TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

    # change to intended input here
    __a :  Union[str, Any]						=  _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]   )
    __a :  Dict						=  _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]   )
    __a :  List[str]						=  prepare_led_inputs_dict(model.config					,				snake_case_					,				snake_case_   )
    __a :  List[str]						=  model(**snake_case_   )[0]
    __a :  Any						=  (1, 1_0_2_4, 7_6_8)
    self.assertEqual(output.shape					,				snake_case_   )
    # change to expected output here
    __a :  Dict						=  tf.convert_to_tensor(
        [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]					,				)
    tf.debugging.assert_near(output[:, :3, :3]					,				snake_case_					,				atol=1E-3   )







   def       lowerCAmelCase			(self :					int   ):
    __a :  Optional[Any]						=  TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

    # change to intended input here
    __a :  int						=  _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]   )
    __a :  Tuple						=  _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]   )
    __a :  Dict						=  prepare_led_inputs_dict(model.config					,				snake_case_					,				snake_case_   )
    __a :  List[str]						=  model(**snake_case_   )[0]
    __a :  List[Any]						=  (1, 1_0_2_4, model.config.vocab_size)
    self.assertEqual(output.shape					,				snake_case_   )
    # change to expected output here
    __a :  str						=  tf.convert_to_tensor(
        [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]					,				)
    tf.debugging.assert_near(output[:, :3, :3]					,				snake_case_					,				atol=1E-3					,				rtol=1E-3   )
