



"""simple docstring"""






import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


snake_case   					=				logging.getLogger(__name__)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[Any]:
  # save results
  if os.path.exists(lowerCAmelCase_	):
    if os.path.exists(os.path.join(lowerCAmelCase_  ,						'''config.json'''	)	) and os.path.isfile(
        os.path.join(lowerCAmelCase_  ,						'''config.json'''	)	):
      os.remove(os.path.join(lowerCAmelCase_  ,						'''config.json'''	)	)
    if os.path.exists(os.path.join(lowerCAmelCase_  ,						'''pytorch_model.bin'''	)	) and os.path.isfile(
        os.path.join(lowerCAmelCase_  ,						'''pytorch_model.bin'''	)	):
      os.remove(os.path.join(lowerCAmelCase_  ,						'''pytorch_model.bin'''	)	)
  else:
    os.makedirs(lowerCAmelCase_	)
  model.save_pretrained(lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=False	)						->				Union[str, Any]:
  _snake_case   	=      2
  if unlogit:
    _snake_case   	=      torch.pow(lowerCAmelCase_  ,						lowerCAmelCase_	)
  _snake_case   	=      p * torch.log(lowerCAmelCase_	)
  _snake_case   	=      0
  return -plogp.sum(dim=-1	)




def 			snake_case		(		lowerCAmelCase_	)						->				List[Any]:
  logger.info('''lv, h >\t''' + '''\t'''.join(f"""{x + 1}""" for x in range(len(lowerCAmelCase_	)	)	)	)
  for row in range(len(lowerCAmelCase_	)	):
    if tensor.dtype != torch.long:
      logger.info(f"""layer {row + 1}:\t""" + '''\t'''.join(f"""{x:.5f}""" for x in tensor[row].cpu().data	)	)
    else:
      logger.info(f"""layer {row + 1}:\t""" + '''\t'''.join(f"""{x:d}""" for x in tensor[row].cpu().data	)	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=True  ,						lowerCAmelCase_=True  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=False	)						->				List[str]:
  _snake_case	,     _snake_case   	=      model.config.num_hidden_layers, model.config.num_attention_heads
  _snake_case   	=      torch.zeros(lowerCAmelCase_  ,						lowerCAmelCase_	).to(args.device	)
  _snake_case   	=      torch.zeros(lowerCAmelCase_  ,						lowerCAmelCase_	).to(args.device	)

  if head_mask is None:
    _snake_case   	=      torch.ones(lowerCAmelCase_  ,						lowerCAmelCase_	).to(args.device	)

  head_mask.requires_grad_(requires_grad=lowerCAmelCase_	)
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    _snake_case   	=      None

  _snake_case   	=      0.0
  _snake_case   	=      0.0
  for step, inputs in enumerate(tqdm(lowerCAmelCase_  ,						desc='''Iteration'''  ,						disable=args.local_rank not in [-1, 0]	)	):
    _snake_case   	=      tuple(t.to(args.device	) for t in inputs	)
    ((_snake_case)	,     )   	=      inputs

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    _snake_case   	=      model(lowerCAmelCase_  ,						labels=lowerCAmelCase_  ,						head_mask=lowerCAmelCase_	)
    #  (loss), lm_logits, presents, (all hidden_states), (attentions)
    _snake_case	,     _snake_case	,     _snake_case   	=      (
        outputs[0],
        outputs[1],
        outputs[-1],
    )  # Loss and logits are the first, attention the last
    loss.backward()  # Backpropagate to populate the gradients in the head mask
    total_loss += loss.detach().cpu().numpy()
    if compute_entropy:
      for layer, attn in enumerate(lowerCAmelCase_	):
        _snake_case   	=      entropy(attn.detach()  ,						lowerCAmelCase_	)
        attn_entropy[layer] += masked_entropy.sum(-1	).sum(0	).sum(0	).detach()

    if compute_importance:
      head_importance += head_mask.grad.abs().detach()
    tot_tokens += torch.ones_like(lowerCAmelCase_	).float().detach().sum().data

  # Normalize
  attn_entropy /= tot_tokens
  head_importance /= tot_tokens
  # Layerwise importance normalization
  if not args.dont_normalize_importance_by_layer:
    _snake_case   	=      2
    _snake_case   	=      torch.pow(torch.pow(lowerCAmelCase_  ,						lowerCAmelCase_	).sum(-1	)  ,						1 / exponent	)
    head_importance /= norm_by_layer.unsqueeze(-1	) + 1E-20

  if not args.dont_normalize_global_importance:
    _snake_case   	=      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

  # Print matrices
  if compute_entropy:
    logger.info('''Attention entropies'''	)
    print_ad_tensor(lowerCAmelCase_	)
  if compute_importance:
    logger.info('''Head importance scores'''	)
    print_ad_tensor(lowerCAmelCase_	)
  logger.info('''Head ranked by importance scores'''	)
  _snake_case   	=      torch.zeros(head_importance.numel()  ,						dtype=torch.long  ,						device=args.device	)
  _snake_case   	=      torch.arange(
      head_importance.numel()  ,						device=args.device	)
  _snake_case   	=      head_ranks.view_as(lowerCAmelCase_	)
  print_ad_tensor(lowerCAmelCase_	)
  return attn_entropy, head_importance, total_loss




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				List[Any]:
  _snake_case	,     _snake_case	,     _snake_case   	=      compute_heads_importance(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						compute_entropy=lowerCAmelCase_	)
  _snake_case   	=      1 / loss  # instead of downsteam score use the LM loss
  logger.info('''Pruning: original score: %f, threshold: %f'''  ,						lowerCAmelCase_  ,						original_score * args.masking_threshold	)

  _snake_case   	=      torch.ones_like(lowerCAmelCase_	)
  _snake_case   	=      max(1  ,						int(new_head_mask.numel() * args.masking_amount	)	)

  _snake_case   	=      original_score
  while current_score >= original_score * args.masking_threshold:
    _snake_case   	=      new_head_mask.clone().detach()  # save current head mask
    # heads from least important to most - keep only not-masked heads
    _snake_case   	=      float('''Inf'''	)
    _snake_case   	=      head_importance.view(-1	).sort()[1]

    if len(lowerCAmelCase_	) <= num_to_mask:
      print('''BREAK BY num_to_mask'''	)
      break

    # mask heads
    _snake_case   	=      current_heads_to_mask[:num_to_mask]
    logger.info('''Heads to mask: %s'''  ,						str(current_heads_to_mask.tolist()	)	)
    _snake_case   	=      new_head_mask.view(-1	)
    _snake_case   	=      0.0
    _snake_case   	=      new_head_mask.view_as(lowerCAmelCase_	)
    _snake_case   	=      new_head_mask.clone().detach()
    print_ad_tensor(lowerCAmelCase_	)

    # Compute metric and head importance again
    _snake_case	,     _snake_case	,     _snake_case   	=      compute_heads_importance(
        lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						compute_entropy=lowerCAmelCase_  ,						head_mask=lowerCAmelCase_	)
    _snake_case   	=      1 / loss
    logger.info(
        '''Masking: current score: %f, remaining heads %d (%.1f percents)'''  ,						lowerCAmelCase_  ,						new_head_mask.sum()  ,						new_head_mask.sum() / new_head_mask.numel() * 100  ,						)

  logger.info('''Final head mask'''	)
  print_ad_tensor(lowerCAmelCase_	)
  np.save(os.path.join(args.output_dir  ,						'''head_mask.npy'''	)  ,						head_mask.detach().cpu().numpy()	)

  return head_mask




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				List[str]:
  _snake_case   	=      datetime.now()
  _snake_case	,     _snake_case	,     _snake_case   	=      compute_heads_importance(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						compute_entropy=lowerCAmelCase_  ,						compute_importance=lowerCAmelCase_  ,						head_mask=lowerCAmelCase_	)
  _snake_case   	=      1 / loss
  _snake_case   	=      datetime.now() - before_time

  _snake_case   	=      sum(p.numel() for p in model.parameters()	)
  _snake_case   	=      {
      layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase_	)	)
  }

  for k, v in heads_to_prune.items():
    if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	):
      _snake_case   	=      [
          v,
      ]

  assert sum(len(lowerCAmelCase_	) for h in heads_to_prune.values()	) == (1 - head_mask.long()).sum().item()
  model.prune_heads(lowerCAmelCase_	)
  _snake_case   	=      sum(p.numel() for p in model.parameters()	)

  _snake_case   	=      datetime.now()
  _snake_case	,     _snake_case	,     _snake_case   	=      compute_heads_importance(
      lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						compute_entropy=lowerCAmelCase_  ,						compute_importance=lowerCAmelCase_  ,						head_mask=lowerCAmelCase_  ,						actually_pruned=lowerCAmelCase_  ,						)

  _snake_case   	=      1 / loss
  _snake_case   	=      datetime.now() - before_time

  logger.info(
      '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						pruned_num_params / original_num_params * 100  ,						)
  logger.info('''Pruning: score with masking: %f score with pruning: %f'''  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''  ,						original_time / new_time * 100	)
  save_model(lowerCAmelCase_  ,						args.output_dir	)




def 			snake_case		(		)						->				Dict:
  _snake_case   	=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--data_dir'''  ,						default=lowerCAmelCase_  ,						type=lowerCAmelCase_  ,						required=lowerCAmelCase_  ,						help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''  ,						)
  parser.add_argument(
      '''--model_name_or_path'''  ,						default=lowerCAmelCase_  ,						type=lowerCAmelCase_  ,						required=lowerCAmelCase_  ,						help='''Path to pretrained model or model identifier from huggingface.co/models'''  ,						)
  parser.add_argument(
      '''--output_dir'''  ,						default=lowerCAmelCase_  ,						type=lowerCAmelCase_  ,						required=lowerCAmelCase_  ,						help='''The output directory where the model predictions and checkpoints will be written.'''  ,						)

  # Other parameters
  parser.add_argument(
      '''--config_name'''  ,						default=''''''  ,						type=lowerCAmelCase_  ,						help='''Pretrained config name or path if not the same as model_name_or_path'''  ,						)
  parser.add_argument(
      '''--tokenizer_name'''  ,						default=''''''  ,						type=lowerCAmelCase_  ,						help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''  ,						)
  parser.add_argument(
      '''--cache_dir'''  ,						default=lowerCAmelCase_  ,						type=lowerCAmelCase_  ,						help='''Where do you want to store the pre-trained models downloaded from s3'''  ,						)
  parser.add_argument(
      '''--data_subset'''  ,						type=lowerCAmelCase_  ,						default=-1  ,						help='''If > 0: limit the data to a subset of data_subset instances.'''	)
  parser.add_argument(
      '''--overwrite_output_dir'''  ,						action='''store_true'''  ,						help='''Whether to overwrite data in output directory'''	)
  parser.add_argument(
      '''--overwrite_cache'''  ,						action='''store_true'''  ,						help='''Overwrite the cached training and evaluation sets'''	)

  parser.add_argument(
      '''--dont_normalize_importance_by_layer'''  ,						action='''store_true'''  ,						help='''Don\'t normalize importance score by layers'''	)
  parser.add_argument(
      '''--dont_normalize_global_importance'''  ,						action='''store_true'''  ,						help='''Don\'t normalize all importance scores between 0 and 1'''  ,						)

  parser.add_argument(
      '''--try_masking'''  ,						action='''store_true'''  ,						help='''Whether to try to mask head until a threshold of accuracy.'''	)
  parser.add_argument(
      '''--masking_threshold'''  ,						default=0.9  ,						type=lowerCAmelCase_  ,						help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''  ,						)
  parser.add_argument(
      '''--masking_amount'''  ,						default=0.1  ,						type=lowerCAmelCase_  ,						help='''Amount to heads to masking at each masking step.'''	)
  parser.add_argument('''--metric_name'''  ,						default='''acc'''  ,						type=lowerCAmelCase_  ,						help='''Metric to use for head masking.'''	)

  parser.add_argument(
      '''--max_seq_length'''  ,						default=128  ,						type=lowerCAmelCase_  ,						help=(
          '''The maximum total input sequence length after WordPiece tokenization. \n'''
          '''Sequences longer than this will be truncated, sequences shorter padded.'''
      )  ,						)
  parser.add_argument('''--batch_size'''  ,						default=1  ,						type=lowerCAmelCase_  ,						help='''Batch size.'''	)

  parser.add_argument('''--seed'''  ,						type=lowerCAmelCase_  ,						default=42	)
  parser.add_argument('''--local_rank'''  ,						type=lowerCAmelCase_  ,						default=-1  ,						help='''local_rank for distributed training on gpus'''	)
  parser.add_argument('''--no_cuda'''  ,						action='''store_true'''  ,						help='''Whether not to use CUDA when available'''	)
  parser.add_argument('''--server_ip'''  ,						type=lowerCAmelCase_  ,						default=''''''  ,						help='''Can be used for distant debugging.'''	)
  parser.add_argument('''--server_port'''  ,						type=lowerCAmelCase_  ,						default=''''''  ,						help='''Can be used for distant debugging.'''	)
  _snake_case   	=      parser.parse_args()

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print('''Waiting for debugger attach'''	)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,						redirect_output=lowerCAmelCase_	)
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    _snake_case   	=      torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''	)
    _snake_case   	=      0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank	)
    _snake_case   	=      torch.device('''cuda'''  ,						args.local_rank	)
    _snake_case   	=      1
    torch.distributed.init_process_group(backend='''nccl'''	)  # Initializes the distributed backend

  # Setup logging
  logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN	)
  logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device  ,						args.n_gpu  ,						bool(args.local_rank != -1	)	)	)

  _snake_case   	=      GPTaLMHeadModel.from_pretrained(args.model_name_or_path	)

  # Distributed and parallel training
  model.to(args.device	)
  if args.local_rank != -1:
    _snake_case   	=      nn.parallel.DistributedDataParallel(
        lowerCAmelCase_  ,						device_ids=[args.local_rank]  ,						output_device=args.local_rank  ,						find_unused_parameters=lowerCAmelCase_	)
  elif args.n_gpu > 1:
    _snake_case   	=      nn.DataParallel(lowerCAmelCase_	)

  # Print/save training arguments
  os.makedirs(args.output_dir  ,						exist_ok=lowerCAmelCase_	)
  torch.save(lowerCAmelCase_  ,						os.path.join(args.output_dir  ,						'''run_args.bin'''	)	)
  logger.info('''Training/evaluation parameters %s'''  ,						lowerCAmelCase_	)

  # Prepare dataset
  _snake_case   	=      np.concatenate(
      [
          np.loadtxt(args.data_dir  ,						dtype=np.intaa	),
      ]	)
  _snake_case   	=      (torch.from_numpy(lowerCAmelCase_	),)
  _snake_case   	=      TensorDataset(*lowerCAmelCase_	)
  _snake_case   	=      RandomSampler(lowerCAmelCase_	)
  _snake_case   	=      DataLoader(lowerCAmelCase_  ,						sampler=lowerCAmelCase_  ,						batch_size=args.batch_size	)

  # Compute head entropy and importance score
  compute_heads_importance(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

  # Try head masking (set heads to zero until the score goes under a threshole)
  # and head pruning (remove masked heads and see the effect on the network)
  if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
    _snake_case   	=      mask_heads(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
    prune_heads(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)


if __name__ == "__main__":
     main()










"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     snake_case   					=				'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->				List[Any]:
  if attention_mask is None:
    _snake_case   	=      np.where(input_ids != config.pad_token_id  ,						1  ,						0	)
  if decoder_attention_mask is None:
    _snake_case   	=      np.where(decoder_input_ids != config.pad_token_id  ,						1  ,						0	)
  if head_mask is None:
    _snake_case   	=      np.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]=1_3							,       __lowerCamelCase    :	int=7							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	Optional[int]=9_9							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Tuple=2							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	Optional[int]=4							,       __lowerCamelCase    :	Tuple="gelu"							,       __lowerCamelCase    :	int=0.1							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Optional[int]=2							,       __lowerCamelCase    :	Optional[Any]=1							,       __lowerCamelCase    :	Dict=0							,       __lowerCamelCase    :	List[str]=0.0_2							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      seq_length
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      eos_token_id
     _snake_case   	=      pad_token_id
     _snake_case   	=      bos_token_id
     _snake_case   	=      initializer_range



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size				)							,       3							,       self.vocab_size				)
     _snake_case   	=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,       dtype=np.intaa				))							,       -1				)

     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)

     _snake_case   	=      BlenderbotConfig(
         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_id=self.eos_token_id							,       bos_token_id=self.bos_token_id							,       pad_token_id=self.pad_token_id							,       initializer_range=self.initializer_range							,       use_cache=__lowerCamelCase							,       )
     _snake_case   	=      prepare_blenderbot_inputs_dict(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.prepare_config_and_inputs()
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,       dtype='''i4'''				)

     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )
     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
         ]							,       axis=-1							,       )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )

     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )
     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_attention_mask=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)



@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Dict           =  99



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ]							,       dtype=np.intaa							,       )

     _snake_case   	=      input_ids.shape[0]
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=2_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=3_2							,       decoder_ffn_dim=3_2							,       max_position_embeddings=4_8							,       eos_token_id=2							,       pad_token_id=1							,       bos_token_id=0							,       )
     return config, input_ids, batch_size



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case   	=      self._get_config_and_data()
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase				)
     _snake_case   	=      (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=1_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=8							,       decoder_ffn_dim=8							,       max_position_embeddings=4_8							,       )
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase							,       decoder_input_ids=__lowerCamelCase				)
     _snake_case   	=      (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,       dtype=np.intaa				)
     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     self.assertEqual(shifted.shape							,       input_ids.shape				)
     self.assertEqual(__lowerCamelCase							,       n_pad_before - 1				)
     self.assertTrue(np.equal(shifted[:, 0]							,       2				).all()				)



@require_flax
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase,__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  True
   A__		:	int           =  (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A__		:	List[Any]           =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      FlaxBlenderbotModelTester(self				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)
         _snake_case   	=      model_class(__lowerCamelCase				)

         @jax.jit
         def encode_jitted(__lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]=None							,       **__lowerCamelCase    :	Union[str, Any]				):
           return model.encode(input_ids=__lowerCamelCase							,       attention_mask=__lowerCamelCase				)

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      model_class(__lowerCamelCase				)
         _snake_case   	=      model.encode(inputs_dict['''input_ids''']							,       inputs_dict['''attention_mask''']				)

         _snake_case   	=      {
             '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
             '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
             '''encoder_outputs''': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(__lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):
           return model.decode(
               decoder_input_ids=__lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       encoder_outputs=__lowerCamelCase							,       )

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_class_name in self.all_model_classes:
       _snake_case   	=      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       _snake_case   	=      np.ones((1, 1)				) * model.config.eos_token_id
       _snake_case   	=      model(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)





   @unittest.skipUnless(jax_device != '''cpu'''							,       '''3B test too slow on CPU.'''				)
   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
     _snake_case   	=      {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

     _snake_case   	=      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''							,       from_pt=__lowerCamelCase				)
     _snake_case   	=      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

     _snake_case   	=      ['''Sam''']
     _snake_case   	=      tokenizer(__lowerCamelCase							,       return_tensors='''jax'''				)

     _snake_case   	=      model.generate(**__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      '''Sam is a great name. It means "sun" in Gaelic.'''

     _snake_case   	=      tokenizer.batch_decode(__lowerCamelCase							,       **__lowerCamelCase				)
     assert generated_txt[0].strip() == tgt_text





