

import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


a_     :    Union[str, Any]     		=   "\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n"

a_     :    Union[str, Any]     		=   "\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"

a_     :    List[str]     		=   "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"precision\": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'precision@10': 1.0}\n\n"


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	List[str]						)		->				Tuple:
 '''simple docstring'''


 return float((preds == labels).mean()						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      simple_accuracy(_UpperCamelCase		,				_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      float(fa_score(y_true=_UpperCamelCase		,				y_pred=_UpperCamelCase						)						)
 return {
     "accuracy": acc,
     "f1": fa,
 }


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      np.array(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      np.array(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      en_sentvecs.shape[0]

 # mean centering
 SCREAMING_SNAKE_CASE		    =      en_sentvecs - np.mean(_UpperCamelCase		,				axis=0						)
 SCREAMING_SNAKE_CASE		    =      in_sentvecs - np.mean(_UpperCamelCase		,				axis=0						)

 SCREAMING_SNAKE_CASE		    =      cdist(_UpperCamelCase		,				_UpperCamelCase		,				'cosine'						)
 SCREAMING_SNAKE_CASE		    =      np.array(range(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      sim.argsort(axis=1						)[:, :10]
 SCREAMING_SNAKE_CASE		    =      np.any(preds == actual[:, None]		,				axis=1						)
 return float(matches.mean()						)




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,      _KWARGS_DESCRIPTION )
class   UpperCamelCase			(  datasets.Metric ):

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     if self.config_name not in [
         "wnli",
         "copa",
         "sna",
         "csqa",
         "wstp",
         "inltkh",
         "bbca",
         "cvit-mkb-clsr",
         "iitp-mr",
         "iitp-pr",
         "actsa-sc",
         "md",
         "wiki-ner",
     ]:
      raise KeyError(
          'You should supply a configuration name selected in '
          '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
          '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
          '"wiki-ner"]'     )
     return datasets.MetricInfo(
         description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
             {
                 'predictions': datasets.Value('int64'     )
                 if self.config_name != 'cvit-mkb-clsr'
                 else datasets.Sequence(datasets.Value('float32'     )     ),
                 'references': datasets.Value('int64'     )
                 if self.config_name != 'cvit-mkb-clsr'
                 else datasets.Sequence(datasets.Value('float32'     )     ),
             }     )				,     codebase_urls=[]				,     reference_urls=[]				,     format='numpy' if self.config_name != 'cvit-mkb-clsr' else None				,     )




    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   str				,     snake_case__			:   Optional[int]     ):

     """simple docstring"""






     if self.config_name == "cvit-mkb-clsr":
      return {"precision@10": precision_at_aa(snake_case__				,     snake_case__     )}
     elif self.config_name in ["wiki-ner"]:
      return acc_and_fa(snake_case__				,     snake_case__     )
     elif self.config_name in [
         "wnli",
         "copa",
         "sna",
         "csqa",
         "wstp",
         "inltkh",
         "bbca",
         "iitp-mr",
         "iitp-pr",
         "actsa-sc",
         "md",
     ]:
      return {"accuracy": simple_accuracy(snake_case__				,     snake_case__     )}
     else:
      raise KeyError(
          'You should supply a configuration name selected in '
          '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
          '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
          '"wiki-ner"]'     )




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 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	int=None		,				_UpperCamelCase		:	Optional[Any]=None		,				_UpperCamelCase		:	List[Any]=None		,				)		->				Tuple:
 '''simple docstring'''


 if attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      input_ids.ne(config.pad_token_id						)
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      decoder_input_ids.ne(config.pad_token_id						)
 if head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.encoder_layers		,				config.encoder_attention_heads		,				device=_UpperCamelCase						)
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=_UpperCamelCase						)
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      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   UpperCamelCase			:

    def __init__(      self			:   Optional[Any]				,     snake_case__			:   List[str]				,     snake_case__			:   Any=1_3				,     snake_case__			:   List[str]=7				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Tuple=False				,     snake_case__			:   Optional[int]=9_9				,     snake_case__			:   List[str]=1_6				,     snake_case__			:   int=2				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   str=4				,     snake_case__			:   Dict="relu"				,     snake_case__			:   Tuple=0.1				,     snake_case__			:   Union[str, Any]=0.1				,     snake_case__			:   Any=0.0				,     snake_case__			:   Dict=0.0				,     snake_case__			:   Optional[Any]=2_0				,     snake_case__			:   int=2				,     snake_case__			:   Optional[Any]=1				,     snake_case__			:   Any=0				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      seq_length
     SCREAMING_SNAKE_CASE		    =      is_training
     SCREAMING_SNAKE_CASE		    =      use_labels
     SCREAMING_SNAKE_CASE		    =      vocab_size
     SCREAMING_SNAKE_CASE		    =      hidden_size
     SCREAMING_SNAKE_CASE		    =      num_hidden_layers
     SCREAMING_SNAKE_CASE		    =      num_attention_heads
     SCREAMING_SNAKE_CASE		    =      intermediate_size
     SCREAMING_SNAKE_CASE		    =      hidden_act
     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      encoder_layerdrop
     SCREAMING_SNAKE_CASE		    =      decoder_layerdrop
     SCREAMING_SNAKE_CASE		    =      max_position_embeddings
     SCREAMING_SNAKE_CASE		    =      eos_token_id
     SCREAMING_SNAKE_CASE		    =      pad_token_id
     SCREAMING_SNAKE_CASE		    =      bos_token_id

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      self.eos_token_id  # Eos Token
     SCREAMING_SNAKE_CASE		    =      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
     SCREAMING_SNAKE_CASE		    =      input_ids.clamp(self.pad_token_id + 1     )
     SCREAMING_SNAKE_CASE		    =      decoder_input_ids.clamp(self.pad_token_id + 1     )

     SCREAMING_SNAKE_CASE		    =      self.get_config()
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(snake_case__				,     snake_case__				,     snake_case__     )
     return config, inputs_dict

    def UpperCamelCase      (      self			:   int     ):

     """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 UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.prepare_config_and_inputs()
     return config, inputs_dict

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel(config=snake_case__     ).get_decoder().to(snake_case__     ).eval()
     SCREAMING_SNAKE_CASE		    =      inputs_dict['input_ids']
     SCREAMING_SNAKE_CASE		    =      inputs_dict['attention_mask']
     SCREAMING_SNAKE_CASE		    =      inputs_dict['head_mask']

     # first forward pass
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     head_mask=snake_case__				,     use_cache=snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      outputs.to_tuple()

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     config.vocab_size     )
     SCREAMING_SNAKE_CASE		    =      ids_tensor((self.batch_size, 3)				,     2     )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE		    =      torch.cat([input_ids, next_tokens]				,     dim=-1     )
     SCREAMING_SNAKE_CASE		    =      torch.cat([attention_mask, next_attn_mask]				,     dim=-1     )

     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__     )['last_hidden_state']
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     past_key_values=snake_case__     )[
         'last_hidden_state'
     ]

     # select random slice
     SCREAMING_SNAKE_CASE		    =      ids_tensor((1,)				,     output_from_past.shape[-1]     ).item()
     SCREAMING_SNAKE_CASE		    =      output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE		    =      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(snake_case__				,     snake_case__				,     atol=1E-2     )     )




    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Any				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel(config=snake_case__     ).to(snake_case__     ).eval()
     SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )

     SCREAMING_SNAKE_CASE		    =      outputs.encoder_last_hidden_state
     SCREAMING_SNAKE_CASE		    =      outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE		    =      model.get_encoder()
      encoder.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      MaMaaaEncoder.from_pretrained(snake_case__     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      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:
      SCREAMING_SNAKE_CASE		    =      model.get_decoder()
      decoder.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      MaMaaaDecoder.from_pretrained(snake_case__     ).to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      decoder(
         input_ids=inputs_dict['decoder_input_ids']				,     attention_mask=inputs_dict['decoder_attention_mask']				,     encoder_hidden_states=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   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    __UpperCamelCase    =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    __UpperCamelCase    =(
        {
            "conversational": MaMaaaForConditionalGeneration,
            "feature-extraction": MaMaaaModel,
            "summarization": MaMaaaForConditionalGeneration,
            "text2text-generation": MaMaaaForConditionalGeneration,
            "translation": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase    =True
    __UpperCamelCase    =True
    __UpperCamelCase    =False
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Optional[int]				,     snake_case__			:   Optional[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 UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__     )

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

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(snake_case__     )
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      model_class.from_pretrained(snake_case__				,     output_loading_info=snake_case__     )
      self.assertEqual(info['missing_keys']				,     []     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case__     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*snake_case__     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
      SCREAMING_SNAKE_CASE		    =      model_class(snake_case__     )
      model.to(snake_case__     )
      model.eval()

      SCREAMING_SNAKE_CASE		    =      copy.deepcopy(self._prepare_for_class(snake_case__				,     snake_case__     )     )

      if not self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE		    =      inputs['input_ids']
       del inputs["input_ids"]
      else:
       SCREAMING_SNAKE_CASE		    =      inputs['input_ids']
       SCREAMING_SNAKE_CASE		    =      inputs.get('decoder_input_ids'				,     snake_case__     )
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'				,     snake_case__     )

      SCREAMING_SNAKE_CASE		    =      model.get_input_embeddings()
      if not self.is_encoder_decoder:
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )
      else:
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      wte(snake_case__     )

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




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE		    =      input_dict['input_ids']
     SCREAMING_SNAKE_CASE		    =      input_ids.ne(1     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration(snake_case__     ).eval().to(snake_case__     )
     if torch_device == "cuda":
      model.half()
     model.generate(snake_case__				,     attention_mask=snake_case__     )
     model.generate(num_beams=4				,     do_sample=snake_case__				,     early_stopping=snake_case__				,     num_return_sequences=3     )


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 return torch.tensor(_UpperCamelCase		,				dtype=torch.long		,				device=_UpperCamelCase						)


a_     :    Optional[int]     		=   1e-4




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

    @cached_property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaModel.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]     )
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(model.config				,     snake_case__				,     snake_case__     )
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 1_1, 1_0_2_4)     )
     self.assertEqual(output.shape				,     snake_case__     )
     # change to expected output here
     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]				,     device=snake_case__     )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case__				,     atol=snake_case__     )     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )

     # change to intended input
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]     )
     SCREAMING_SNAKE_CASE		    =      _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]     )
     SCREAMING_SNAKE_CASE		    =      prepare_mam_aaa_inputs_dict(model.config				,     snake_case__				,     snake_case__     )
     with torch.no_grad():
      SCREAMING_SNAKE_CASE		    =      model(**snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      torch.Size((1, 1_1, model.config.vocab_size)     )
     self.assertEqual(output.shape				,     snake_case__     )
     # change to expected output here
     SCREAMING_SNAKE_CASE		    =      torch.tensor(
         [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]				,     device=snake_case__     )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,     snake_case__				,     atol=snake_case__     )     )




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

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'				,     src_lang='fr'				,     tgt_lang='en'     )

     SCREAMING_SNAKE_CASE		    =      [
         '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
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__				,     padding=snake_case__				,     return_tensors='pt'     )

     SCREAMING_SNAKE_CASE		    =      model.generate(
         input_ids=dct['input_ids'].to(snake_case__     )				,     attention_mask=dct['attention_mask'].to(snake_case__     )				,     num_beams=5				,     forced_bos_token_id=tokenizer.get_lang_id('en'     )				,     )

     SCREAMING_SNAKE_CASE		    =      [
         '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.',
     ]

     SCREAMING_SNAKE_CASE		    =      tokenizer.batch_decode(
         hypotheses_batch.tolist()				,     clean_up_tokenization_spaces=snake_case__				,     skip_special_tokens=snake_case__     )
     assert generated == expected_en

