



"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A:     Optional[Any]										=	logging.getLogger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[Any]		=		'token-classification'

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    if type(_SCREAMING_SNAKE_CASE		) == dict:
     UpperCAmelCase			:      Union[str, Any]			=			Namespace(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			import_module("""tasks"""		)
    try:
     UpperCAmelCase			:      Any			=			getattr(_SCREAMING_SNAKE_CASE   ,  hparams.task_type		)
     UpperCAmelCase			:      TokenClassificationTask			=			token_classification_task_clazz()
    except AttributeError:
     raise ValueError(
         F"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
         F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"		)
    UpperCAmelCase			:      List[str]			=			self.token_classification_task.get_labels(hparams.labels		)
    UpperCAmelCase			:      Dict			=			CrossEntropyLoss().ignore_index
    super().__init__(_SCREAMING_SNAKE_CASE   ,  len(self.labels		)   ,  self.mode		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    return self.model(**_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     UpperCAmelCase			:      Dict			=			(
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids

    UpperCAmelCase			:      Optional[Any]			=			self(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			outputs[0]
    # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
    return {"loss": loss}

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.hparams
    for mode in ["train", "dev", "test"]:
     UpperCAmelCase			:      List[Any]			=			self._feature_file(_SCREAMING_SNAKE_CASE		)
     if os.path.exists(_SCREAMING_SNAKE_CASE		) and not args.overwrite_cache:
      logger.info("""Loading features from cached file %s"""   ,  _SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Optional[Any]			=			torch.load(_SCREAMING_SNAKE_CASE		)
     else:
      logger.info("""Creating features from dataset file at %s"""   ,  args.data_dir		)
      UpperCAmelCase			:      int			=			self.token_classification_task.read_examples_from_file(args.data_dir   ,  _SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Dict			=			self.token_classification_task.convert_examples_to_features(
          _SCREAMING_SNAKE_CASE   ,  self.labels   ,  args.max_seq_length   ,  self.tokenizer   ,  cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]		)   ,  cls_token=self.tokenizer.cls_token   ,  cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0   ,  sep_token=self.tokenizer.sep_token   ,  sep_token_extra=_SCREAMING_SNAKE_CASE   ,  pad_on_left=bool(self.config.model_type in ["""xlnet"""]		)   ,  pad_token=self.tokenizer.pad_token_id   ,  pad_token_segment_id=self.tokenizer.pad_token_type_id   ,  pad_token_label_id=self.pad_token_label_id   ,  )
      logger.info("""Saving features into cached file %s"""   ,  _SCREAMING_SNAKE_CASE		)
      torch.save(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = False		)						->   DataLoader:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self._feature_file(_SCREAMING_SNAKE_CASE		)
    logger.info("""Loading features from cached file %s"""   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			torch.load(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			torch.tensor([f.input_ids for f in features]   ,  dtype=torch.long		)
    UpperCAmelCase			:      Tuple			=			torch.tensor([f.attention_mask for f in features]   ,  dtype=torch.long		)
    if features[0].token_type_ids is not None:
     UpperCAmelCase			:      List[str]			=			torch.tensor([f.token_type_ids for f in features]   ,  dtype=torch.long		)
    else:
     UpperCAmelCase			:      Any			=			torch.tensor([0 for f in features]   ,  dtype=torch.long		)
     # HACK(we will not use this anymore soon)
    UpperCAmelCase			:      Any			=			torch.tensor([f.label_ids for f in features]   ,  dtype=torch.long		)
    return DataLoader(
        TensorDataset(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  batch_size=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    """Compute validation""" ""
    UpperCAmelCase			:      Optional[Any]			=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     UpperCAmelCase			:      List[Any]			=			(
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids
    UpperCAmelCase			:      int			=			self(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			outputs[:2]
    UpperCAmelCase			:      Dict			=			logits.detach().cpu().numpy()
    UpperCAmelCase			:      Optional[Any]			=			inputs["""labels"""].detach().cpu().numpy()
    return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			torch.stack([x["""val_loss"""] for x in outputs]		).mean()
    UpperCAmelCase			:      int			=			np.concatenate([x["""pred"""] for x in outputs]   ,  axis=0		)
    UpperCAmelCase			:      Optional[int]			=			np.argmax(_SCREAMING_SNAKE_CASE   ,  axis=2		)
    UpperCAmelCase			:      Dict			=			np.concatenate([x["""target"""] for x in outputs]   ,  axis=0		)

    UpperCAmelCase			:      Optional[int]			=			dict(enumerate(self.labels		)		)
    UpperCAmelCase			:      Any			=			[[] for _ in range(out_label_ids.shape[0]		)]
    UpperCAmelCase			:      Dict			=			[[] for _ in range(out_label_ids.shape[0]		)]

    for i in range(out_label_ids.shape[0]		):
     for j in range(out_label_ids.shape[1]		):
      if out_label_ids[i, j] != self.pad_token_label_id:
       out_label_list[i].append(label_map[out_label_ids[i][j]]		)
       preds_list[i].append(label_map[preds[i][j]]		)

    UpperCAmelCase			:      int			=			{
        """val_loss""": val_loss_mean,
        """accuracy_score""": accuracy_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
        """precision""": precision_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
        """recall""": recall_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
        """f1""": fa_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
    }

    UpperCAmelCase			:      int			=			dict(results.items()		)
    UpperCAmelCase			:      Optional[int]			=			results
    return ret, preds_list, out_label_list

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			self._eval_end(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			ret["""log"""]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			self._eval_end(_SCREAMING_SNAKE_CASE		)

    # Converting to the dict required by pl
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
    # pytorch_lightning/trainer/logging.py#L139
    UpperCAmelCase			:      Any			=			ret["""log"""]
    # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
    return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




   @staticmethod
   def 			SCREAMING_SNAKE_CASE  (     _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    BaseTransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    parser.add_argument(
        """--task_type"""   ,  default="""NER"""   ,  type=_SCREAMING_SNAKE_CASE   ,  help="""Task type to fine tune in training (e.g. NER, POS, etc)"""		)
    parser.add_argument(
        """--max_seq_length"""   ,  default=128   ,  type=_SCREAMING_SNAKE_CASE   ,  help=(
            """The maximum total input sequence length after tokenization. Sequences longer """
            """than this will be truncated, sequences shorter will be padded."""
        )   ,  )

    parser.add_argument(
        """--labels"""   ,  default=""""""   ,  type=_SCREAMING_SNAKE_CASE   ,  help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""   ,  )
    parser.add_argument(
        """--gpus"""   ,  default=0   ,  type=_SCREAMING_SNAKE_CASE   ,  help="""The number of GPUs allocated for this, it is by default 0 meaning none"""   ,  )

    parser.add_argument(
        """--overwrite_cache"""   ,  action="""store_true"""   ,  help="""Overwrite the cached training and evaluation sets"""		)

    return parser


if __name__ == "__main__":
      A:     List[str]										=	argparse.ArgumentParser()
      add_generic_args(parser, os.getcwd())
      A:     Optional[int]										=	NERTransformer.add_model_specific_args(parser, os.getcwd())
      A:     Tuple										=	parser.parse_args()
      A:     Optional[int]										=	NERTransformer(args)
      A:     Dict										=	generic_train(model, args)

      if args.do_predict:
            # See https://github.com/huggingface/transformers/issues/3159
            # pl use this default format to create a checkpoint:
            # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
            # /pytorch_lightning/callbacks/model_checkpoint.py#L322
            A:     Union[str, Any]										=	sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
            A:     Union[str, Any]										=	model.load_from_checkpoint(checkpoints[-1])
            trainer.test(model)





"""simple docstring"""
import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
      from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder

def    _snake_case		(		UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Any=None    ,	UpperCamelCase		:	Union[str, Any]=None    ,	UpperCamelCase		:	Tuple=None    ,	UpperCamelCase		:	Optional[int]=None    ,	UpperCamelCase		:	int=None    ,	):
 if attention_mask is None:
  UpperCAmelCase			:      Optional[int]			=			input_ids.ne(config.pad_token_id		)
 if decoder_attention_mask is None:
  UpperCAmelCase			:      int			=			decoder_input_ids.ne(config.pad_token_id		)
 if head_mask is None:
  UpperCAmelCase			:      Union[str, Any]			=			torch.ones(config.encoder_layers    ,	config.encoder_attention_heads    ,	device=UpperCamelCase		)
 if decoder_head_mask is None:
  UpperCAmelCase			:      List[str]			=			torch.ones(config.decoder_layers    ,	config.decoder_attention_heads    ,	device=UpperCamelCase		)
 if cross_attn_head_mask is None:
  UpperCAmelCase			:      Any			=			torch.ones(config.decoder_layers    ,	config.decoder_attention_heads    ,	device=UpperCamelCase		)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=13   ,  _SCREAMING_SNAKE_CASE=7   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=99   ,  _SCREAMING_SNAKE_CASE=16   ,  _SCREAMING_SNAKE_CASE=2   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE="relu"   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=20   ,  _SCREAMING_SNAKE_CASE=2   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=0   ,  )						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			parent
    UpperCAmelCase			:      str			=			batch_size
    UpperCAmelCase			:      Optional[int]			=			seq_length
    UpperCAmelCase			:      Any			=			is_training
    UpperCAmelCase			:      str			=			use_labels
    UpperCAmelCase			:      Union[str, Any]			=			vocab_size
    UpperCAmelCase			:      List[str]			=			hidden_size
    UpperCAmelCase			:      int			=			num_hidden_layers
    UpperCAmelCase			:      List[Any]			=			num_attention_heads
    UpperCAmelCase			:      str			=			intermediate_size
    UpperCAmelCase			:      int			=			hidden_act
    UpperCAmelCase			:      Optional[Any]			=			hidden_dropout_prob
    UpperCAmelCase			:      Union[str, Any]			=			attention_probs_dropout_prob
    UpperCAmelCase			:      Optional[Any]			=			encoder_layerdrop
    UpperCAmelCase			:      Tuple			=			decoder_layerdrop
    UpperCAmelCase			:      int			=			max_position_embeddings
    UpperCAmelCase			:      List[Any]			=			eos_token_id
    UpperCAmelCase			:      Tuple			=			pad_token_id
    UpperCAmelCase			:      Dict			=			bos_token_id

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size		)
    UpperCAmelCase			:      List[str]			=			self.eos_token_id  # Eos Token
    UpperCAmelCase			:      Any			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size		)

    # we need to clamp the input ids here to avoid having pad token in between
    # this is because for M2M100 the position_ids are prepared such that
    # all pad tokens have pos id = 2 and rest are between 2..seq_length
    # and the seq_length here is seq_length - num_pad_tokens
    # but when using past, there is no way of knowing if the past input ids had
    # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
    # position_ids being off by num_pad_tokens in past input
    UpperCAmelCase			:      int			=			input_ids.clamp(self.pad_token_id + 1		)
    UpperCAmelCase			:      Dict			=			decoder_input_ids.clamp(self.pad_token_id + 1		)

    UpperCAmelCase			:      Optional[Any]			=			self.get_config()
    UpperCAmelCase			:      int			=			prepare_mam_aaa_inputs_dict(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    return config, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    return MaMaaaConfig(
        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   ,  encoder_layerdrop=self.encoder_layerdrop   ,  decoder_layerdrop=self.decoder_layerdrop   ,  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   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			self.prepare_config_and_inputs()
    return config, inputs_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			MaMaaaModel(config=_SCREAMING_SNAKE_CASE		).get_decoder().to(_SCREAMING_SNAKE_CASE		).eval()
    UpperCAmelCase			:      str			=			inputs_dict["""input_ids"""]
    UpperCAmelCase			:      Tuple			=			inputs_dict["""attention_mask"""]
    UpperCAmelCase			:      Dict			=			inputs_dict["""head_mask"""]

    # first forward pass
    UpperCAmelCase			:      List[str]			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  head_mask=_SCREAMING_SNAKE_CASE   ,  use_cache=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			outputs.to_tuple()

    # create hypothetical multiple next token and extent to next_input_ids
    UpperCAmelCase			:      int			=			ids_tensor((self.batch_size, 3)   ,  config.vocab_size		)
    UpperCAmelCase			:      Optional[Any]			=			ids_tensor((self.batch_size, 3)   ,  2		)

    # append to next input_ids and
    UpperCAmelCase			:      Union[str, Any]			=			torch.cat([input_ids, next_tokens]   ,  dim=-1		)
    UpperCAmelCase			:      Tuple			=			torch.cat([attention_mask, next_attn_mask]   ,  dim=-1		)

    UpperCAmelCase			:      Optional[int]			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE		)["""last_hidden_state"""]
    UpperCAmelCase			:      Tuple			=			model(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  past_key_values=_SCREAMING_SNAKE_CASE		)[
        """last_hidden_state"""
    ]

    # select random slice
    UpperCAmelCase			:      Union[str, Any]			=			ids_tensor((1,)   ,  output_from_past.shape[-1]		).item()
    UpperCAmelCase			:      Any			=			output_from_no_past[:, -3:, random_slice_idx].detach()
    UpperCAmelCase			:      Optional[Any]			=			output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  atol=1E-2		)		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			MaMaaaModel(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).eval()
    UpperCAmelCase			:      Any			=			model(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			outputs.encoder_last_hidden_state
    UpperCAmelCase			:      List[str]			=			outputs.last_hidden_state

    with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase			:      Tuple			=			model.get_encoder()
     encoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[str]			=			MaMaaaEncoder.from_pretrained(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			encoder(inputs_dict["""input_ids"""]   ,  attention_mask=inputs_dict["""attention_mask"""]		)[
        0
    ]

    self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

    with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase			:      Optional[int]			=			model.get_decoder()
     decoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Tuple			=			MaMaaaDecoder.from_pretrained(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			decoder(
        input_ids=inputs_dict["""decoder_input_ids"""]   ,  attention_mask=inputs_dict["""decoder_attention_mask"""]   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  encoder_attention_mask=inputs_dict["""attention_mask"""]   ,  )[0]

    self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)



@require_torch
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__   ,    UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Optional[int]		=		(
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase						:     Tuple		=		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   __lowerCAmelCase						:     str		=		(
       {
           'conversational': MaMaaaForConditionalGeneration,
           'feature-extraction': MaMaaaModel,
           'summarization': MaMaaaForConditionalGeneration,
           'text2text-generation': MaMaaaForConditionalGeneration,
           'translation': MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase						:     str		=		True
   __lowerCAmelCase						:     Tuple		=		True
   __lowerCAmelCase						:     Tuple		=		False
   __lowerCAmelCase						:     int		=		False

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if pipeline_test_casse_name == "TranslationPipelineTests":
     # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
     # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
     return True

    return False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			MaMaaaModelTester(self		)
    UpperCAmelCase			:      List[str]			=			ConfigTester(self   ,  config_class=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     UpperCAmelCase			:      Dict			=			model_class(_SCREAMING_SNAKE_CASE		)

     with tempfile.TemporaryDirectory() as tmpdirname:
      model.save_pretrained(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			model_class.from_pretrained(_SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE		)
     self.assertEqual(info["""missing_keys"""]   ,  []		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
     UpperCAmelCase			:      List[Any]			=			model_class(_SCREAMING_SNAKE_CASE		)
     model.to(_SCREAMING_SNAKE_CASE		)
     model.eval()

     UpperCAmelCase			:      str			=			copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)		)

     if not self.is_encoder_decoder:
      UpperCAmelCase			:      Optional[Any]			=			inputs["""input_ids"""]
      del inputs["input_ids"]
     else:
      UpperCAmelCase			:      Optional[int]			=			inputs["""input_ids"""]
      UpperCAmelCase			:      str			=			inputs.get("""decoder_input_ids"""   ,  _SCREAMING_SNAKE_CASE		)
      del inputs["input_ids"]
      inputs.pop("""decoder_input_ids"""   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Dict			=			model.get_input_embeddings()
     if not self.is_encoder_decoder:
      UpperCAmelCase			:      Union[str, Any]			=			wte(_SCREAMING_SNAKE_CASE		)
     else:
      UpperCAmelCase			:      List[str]			=			wte(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Optional[Any]			=			wte(_SCREAMING_SNAKE_CASE		)

     with torch.no_grad():
      model(**_SCREAMING_SNAKE_CASE		)[0]




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			:      Any			=			self.model_tester.prepare_config_and_inputs()
    UpperCAmelCase			:      Tuple			=			input_dict["""input_ids"""]
    UpperCAmelCase			:      Union[str, Any]			=			input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE		).eval().to(_SCREAMING_SNAKE_CASE		)
    if torch_device == "cuda":
     model.half()
    model.generate(_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE		)
    model.generate(num_beams=4   ,  do_sample=_SCREAMING_SNAKE_CASE   ,  early_stopping=_SCREAMING_SNAKE_CASE   ,  num_return_sequences=3		)

def    _snake_case		(		UpperCamelCase		:	Any		):
 return torch.tensor(UpperCamelCase    ,	dtype=torch.long    ,	device=UpperCamelCase		)


A:     int										=	1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   @cached_property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
    UpperCAmelCase			:      List[Any]			=			_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
    UpperCAmelCase			:      Optional[int]			=			prepare_mam_aaa_inputs_dict(model.config   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    with torch.no_grad():
     UpperCAmelCase			:      Union[str, Any]			=			model(**_SCREAMING_SNAKE_CASE		)[0]
    UpperCAmelCase			:      Any			=			torch.Size((1, 11, 1024)		)
    self.assertEqual(output.shape   ,  _SCREAMING_SNAKE_CASE		)
    # change to expected output here
    UpperCAmelCase			:      Dict			=			torch.tensor(
        [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]   ,  device=_SCREAMING_SNAKE_CASE		)
    self.assertTrue(torch.allclose(output[:, :3, :3]   ,  _SCREAMING_SNAKE_CASE   ,  atol=_SCREAMING_SNAKE_CASE		)		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""		).to(_SCREAMING_SNAKE_CASE		)

    # change to intended input
    UpperCAmelCase			:      Dict			=			_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
    UpperCAmelCase			:      List[str]			=			_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
    UpperCAmelCase			:      int			=			prepare_mam_aaa_inputs_dict(model.config   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    with torch.no_grad():
     UpperCAmelCase			:      List[Any]			=			model(**_SCREAMING_SNAKE_CASE		)[0]
    UpperCAmelCase			:      Union[str, Any]			=			torch.Size((1, 11, model.config.vocab_size)		)
    self.assertEqual(output.shape   ,  _SCREAMING_SNAKE_CASE		)
    # change to expected output here
    UpperCAmelCase			:      List[str]			=			torch.tensor(
        [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]   ,  device=_SCREAMING_SNAKE_CASE		)
    self.assertTrue(torch.allclose(output[:, :3, :3]   ,  _SCREAMING_SNAKE_CASE   ,  atol=_SCREAMING_SNAKE_CASE		)		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""		).to(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""   ,  src_lang="""fr"""   ,  tgt_lang="""en"""		)

    UpperCAmelCase			:      List[Any]			=			[
        """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
        """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
        """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
        """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
        """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
    ]

    # The below article tests that we don't add any hypotheses outside of the top n_beams
    UpperCAmelCase			:      Any			=			tokenizer(_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""		)

    UpperCAmelCase			:      str			=			model.generate(
        input_ids=dct["""input_ids"""].to(_SCREAMING_SNAKE_CASE		)   ,  attention_mask=dct["""attention_mask"""].to(_SCREAMING_SNAKE_CASE		)   ,  num_beams=5   ,  forced_bos_token_id=tokenizer.get_lang_id("""en"""		)   ,  )

    UpperCAmelCase			:      Tuple			=			[
        """The NSA case highlights the total absence of intelligence debate""",
        """I think there are two levels of response from the French government.""",
        """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
        """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
        """ communications in France.""",
    ]

    UpperCAmelCase			:      str			=			tokenizer.batch_decode(
        hypotheses_batch.tolist()   ,  clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE   ,  skip_special_tokens=_SCREAMING_SNAKE_CASE		)
    assert generated == expected_en
