'''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 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : Any					,    snake_case_       : List[Any]					,    snake_case_       : Optional[Any]=None					,    snake_case_       : Any=None					,    snake_case_       : Optional[Any]=None					,    snake_case_       : Dict=None					,    snake_case_       : List[Any]=None					,    )    ->      Optional[Any]:


     '''simple docstring'''




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



    def __init__(self    :    Optional[int]   ,			__a    :    List[str]   ,			__a    :    str=13   ,			__a    :    Optional[Any]=7   ,			__a    :    Tuple=True   ,			__a    :    Optional[Any]=False   ,			__a    :    Any=99   ,			__a    :    int=16   ,			__a    :    Optional[Any]=2   ,			__a    :    List[Any]=4   ,			__a    :    Dict=4   ,			__a    :    List[Any]="relu"   ,			__a    :    List[Any]=0.1   ,			__a    :    Any=0.1   ,			__a    :    int=0.0   ,			__a    :    Any=0.0   ,			__a    :    Optional[Any]=20   ,			__a    :    List[Any]=2   ,			__a    :    Tuple=1   ,			__a    :    Any=0   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   seq_length
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   encoder_layerdrop
         UpperCAmelCase_           =   decoder_layerdrop
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   eos_token_id
         UpperCAmelCase_           =   pad_token_id
         UpperCAmelCase_           =   bos_token_id



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size )
         UpperCAmelCase_           =   self.eos_token_id  # Eos Token
         UpperCAmelCase_           =   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_           =   input_ids.clamp(self.pad_token_id + 1 )
         UpperCAmelCase_           =   decoder_input_ids.clamp(self.pad_token_id + 1 )

         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   prepare_mam_aaa_inputs_dict(__a   ,			__a   ,			__a )
         return config, inputs_dict



    def 					_lowercase						(self    :    Optional[Any] ):
         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 					_lowercase						(self    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.prepare_config_and_inputs()
         return config, inputs_dict



    def 					_lowercase						(self    :    int   ,			__a    :    int   ,			__a    :    Union[str, Any] ):
         UpperCAmelCase_           =   MaMaaaModel(config=__a ).get_decoder().to(__a ).eval()
         UpperCAmelCase_           =   inputs_dict["input_ids"]
         UpperCAmelCase_           =   inputs_dict["attention_mask"]
         UpperCAmelCase_           =   inputs_dict["head_mask"]

         # first forward pass
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			head_mask=__a   ,			use_cache=__a )

         UpperCAmelCase_    , UpperCAmelCase_           =   outputs.to_tuple()

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

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

         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a )["last_hidden_state"]
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			past_key_values=__a )[
             "last_hidden_state"
         ]

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






    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Any   ,			__a    :    Optional[int] ):
         UpperCAmelCase_           =   MaMaaaModel(config=__a ).to(__a ).eval()
         UpperCAmelCase_           =   model(**__a )

         UpperCAmelCase_           =   outputs.encoder_last_hidden_state
         UpperCAmelCase_           =   outputs.last_hidden_state

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

         UpperCAmelCase_           =   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_           =   model.get_decoder()
              decoder.save_pretrained(__a )
              UpperCAmelCase_           =   MaMaaaDecoder.from_pretrained(__a ).to(__a )

         UpperCAmelCase_           =   decoder(
             input_ids=inputs_dict["decoder_input_ids"]   ,			attention_mask=inputs_dict["decoder_attention_mask"]   ,			encoder_hidden_states=__a   ,			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        __A      ( UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase	):
    a__       :						List[Any]          =  (
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    a__       :						int          =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    a__       :						Optional[int]          =  (
        {
            """conversational""": MaMaaaForConditionalGeneration,
            """feature-extraction""": MaMaaaModel,
            """summarization""": MaMaaaForConditionalGeneration,
            """text2text-generation""": MaMaaaForConditionalGeneration,
            """translation""": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    a__       :						Union[str, Any]          =  True
    a__       :						Tuple          =  True
    a__       :						List[str]          =  False
    a__       :						Union[str, Any]          =  False



    def 					_lowercase						(self    :    Any   ,			__a    :    Optional[Any]   ,			__a    :    Dict   ,			__a    :    int   ,			__a    :    str   ,			__a    :    str ):
         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 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   MaMaaaModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a )



    def 					_lowercase						(self    :    int ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(__a )

              with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(__a )
                   UpperCAmelCase_    , UpperCAmelCase_           =   model_class.from_pretrained(__a   ,			output_loading_info=__a )
              self.assertEqual(info["missing_keys"]   ,			[] )



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*__a )



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*__a )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
              UpperCAmelCase_           =   model_class(__a )
              model.to(__a )
              model.eval()

              UpperCAmelCase_           =   copy.deepcopy(self._prepare_for_class(__a   ,			__a ) )

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

              UpperCAmelCase_           =   model.get_input_embeddings()
              if not self.is_encoder_decoder:
                   UpperCAmelCase_           =   wte(__a )
              else:
                   UpperCAmelCase_           =   wte(__a )
                   UpperCAmelCase_           =   wte(__a )

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






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





def 	lowerCAmelCase_				(					snake_case_       : List[str]			)    ->      Union[str, Any]:


     '''simple docstring'''




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


SCREAMING_SNAKE_CASE_:     Any  =1E-4






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



    @cached_property
    def 					_lowercase						(self    :    Optional[int] ):
         return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(__a )
         UpperCAmelCase_           =   _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
         UpperCAmelCase_           =   _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
         UpperCAmelCase_           =   prepare_mam_aaa_inputs_dict(model.config   ,			__a   ,			__a )
         with torch.no_grad():
              UpperCAmelCase_           =   model(**__a )[0]
         UpperCAmelCase_           =   torch.Size((1, 11, 1024) )
         self.assertEqual(output.shape   ,			__a )
         # change to expected output here
         UpperCAmelCase_           =   torch.tensor(
             [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]   ,			device=__a )
         self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__a   ,			atol=__a ) )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__a )

         # change to intended input
         UpperCAmelCase_           =   _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
         UpperCAmelCase_           =   _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
         UpperCAmelCase_           =   prepare_mam_aaa_inputs_dict(model.config   ,			__a   ,			__a )
         with torch.no_grad():
              UpperCAmelCase_           =   model(**__a )[0]
         UpperCAmelCase_           =   torch.Size((1, 11, model.config.vocab_size) )
         self.assertEqual(output.shape   ,			__a )
         # change to expected output here
         UpperCAmelCase_           =   torch.tensor(
             [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]   ,			device=__a )
         self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__a   ,			atol=__a ) )






    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__a )
         UpperCAmelCase_           =   MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"   ,			src_lang="fr"   ,			tgt_lang="en" )

         UpperCAmelCase_           =   [
             "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_           =   tokenizer(__a   ,			padding=__a   ,			return_tensors="pt" )

         UpperCAmelCase_           =   model.generate(
             input_ids=dct["input_ids"].to(__a )   ,			attention_mask=dct["attention_mask"].to(__a )   ,			num_beams=5   ,			forced_bos_token_id=tokenizer.get_lang_id("en" )   ,			)

         UpperCAmelCase_           =   [
             "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_           =   tokenizer.batch_decode(
             hypotheses_batch.tolist()   ,			clean_up_tokenization_spaces=__a   ,			skip_special_tokens=__a )
         assert generated == expected_en





'''simple docstring'''





import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


SCREAMING_SNAKE_CASE_:     Optional[Any]  ='\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


SCREAMING_SNAKE_CASE_:     List[Any]  =r'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'




@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION	)
class        __A      ( datasets.Metric	):



    def 					_lowercase						(self    :    Optional[Any] ):
         return datasets.MetricInfo(
             description=_DESCRIPTION   ,			citation=_CITATION   ,			inputs_description=_KWARGS_DESCRIPTION   ,			features=datasets.Features(
                 {
                     "predictions": datasets.Value("string" ),
                     "references": datasets.Value("string" ),
                 } )   ,			homepage="https://github.com/hendrycks/math"   ,			codebase_urls=["https://github.com/hendrycks/math"]   ,			)






    def 					_lowercase						(self    :    Tuple   ,			__a    :    Optional[int]   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   0.0
         for i, j in zip(__a   ,			__a ):
              n_correct += 1.0 if math_equivalence.is_equiv(__a   ,			__a ) else 0.0
         UpperCAmelCase_           =   n_correct / len(__a )
         return {
             "accuracy": accuracy,
         }




