from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class   snake_case_       (		__A       ):
   __A	:		List[Any]         =				"blenderbot-small"
   __A	:		Tuple         =				["past_key_values"]
   __A	:		Union[str, Any]         =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






   def __init__(      self				:     Any	,						lowercase_				:     Any=5_02_65	,						lowercase_				:     Optional[Any]=5_12	,						lowercase_				:     Optional[int]=8	,						lowercase_				:     Tuple=20_48	,						lowercase_				:     Any=16	,						lowercase_				:     Optional[int]=8	,						lowercase_				:     Any=20_48	,						lowercase_				:     Any=16	,						lowercase_				:     Tuple=0.0	,						lowercase_				:     Optional[Any]=0.0	,						lowercase_				:     Union[str, Any]=True	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     int="gelu"	,						lowercase_				:     str=5_12	,						lowercase_				:     str=0.1	,						lowercase_				:     Optional[int]=0.0	,						lowercase_				:     Union[str, Any]=0.0	,						lowercase_				:     Union[str, Any]=0.02	,						lowercase_				:     str=1	,						lowercase_				:     int=False	,						lowercase_				:     Optional[int]=0	,						lowercase_				:     Tuple=1	,						lowercase_				:     int=2	,						lowercase_				:     List[str]=2	,						**lowercase_				:     Tuple	,						)					->						Union[str, Any]:
       lowercase__ :   Any    					=				vocab_size
       lowercase__ :   int    					=				max_position_embeddings
       lowercase__ :   Optional[Any]    					=				d_model
       lowercase__ :   List[str]    					=				encoder_ffn_dim
       lowercase__ :   List[str]    					=				encoder_layers
       lowercase__ :   List[Any]    					=				encoder_attention_heads
       lowercase__ :   List[str]    					=				decoder_ffn_dim
       lowercase__ :   Optional[Any]    					=				decoder_layers
       lowercase__ :   Union[str, Any]    					=				decoder_attention_heads
       lowercase__ :   int    					=				dropout
       lowercase__ :   Optional[int]    					=				attention_dropout
       lowercase__ :   Dict    					=				activation_dropout
       lowercase__ :   Union[str, Any]    					=				activation_function
       lowercase__ :   Dict    					=				init_std
       lowercase__ :   int    					=				encoder_layerdrop
       lowercase__ :   List[str]    					=				decoder_layerdrop
       lowercase__ :   str    					=				use_cache
       lowercase__ :   Dict    					=				encoder_layers
       lowercase__ :   int    					=				scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=lowercase_	,						bos_token_id=lowercase_	,						eos_token_id=lowercase_	,						is_encoder_decoder=lowercase_	,						decoder_start_token_id=lowercase_	,						forced_eos_token_id=lowercase_	,						**lowercase_	,						)



class   snake_case_       (		__A       ):






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Mapping[str, Mapping[int, str]]:
       if self.task in ["default", "seq2seq-lm"]:
           lowercase__ :   str    					=				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]  )

           if self.use_past:
               lowercase__ :   Tuple    					=				{0: "batch"}
               lowercase__ :   Any    					=				{0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
               lowercase__ :   Dict    					=				{0: "batch", 1: "decoder_sequence"}
               lowercase__ :   Tuple    					=				{0: "batch", 1: "decoder_sequence"}

           if self.use_past:
               self.fill_with_past_key_values_(lowercase_	,						direction="inputs"  )
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           lowercase__ :   Optional[int]    					=				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]  )
           if self.use_past:
               lowercase__					,   lowercase__ :   Any    					=				self.num_layers
               for i in range(lowercase_  ):
                   lowercase__ :   List[str]    					=				{0: "batch", 2: "past_sequence + sequence"}
                   lowercase__ :   Any    					=				{0: "batch", 2: "past_sequence + sequence"}
       else:
           lowercase__ :   int    					=				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                   ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
               ]  )

       return common_inputs






   @property
   def       __UpperCamelCase     (      self				:     str  )					->						Mapping[str, Mapping[int, str]]:
       if self.task in ["default", "seq2seq-lm"]:
           lowercase__ :   Dict    					=				super().outputs
       else:
           lowercase__ :   List[str]    					=				super(lowercase_	,						self  ).outputs
           if self.use_past:
               lowercase__					,   lowercase__ :   Optional[Any]    					=				self.num_layers
               for i in range(lowercase_  ):
                   lowercase__ :   Dict    					=				{0: "batch", 2: "past_sequence + sequence"}
                   lowercase__ :   List[Any]    					=				{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs






   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     PreTrainedTokenizer	,						lowercase_				:     int = -1	,						lowercase_				:     int = -1	,						lowercase_				:     bool = False	,						lowercase_				:     Optional[TensorType] = None	,						)					->						Mapping[str, Any]:
       lowercase__ :   Optional[int]    					=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )

       # Generate decoder inputs
       lowercase__ :   str    					=				seq_length if not self.use_past else 1
       lowercase__ :   Optional[Any]    					=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )
       lowercase__ :   Union[str, Any]    					=				{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       lowercase__ :   Union[str, Any]    					=				dict(**lowercase_	,						**lowercase_  )

       if self.use_past:
           if not is_torch_available():
               raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
           else:
               import torch
           lowercase__					,   lowercase__ :   Union[str, Any]    					=				common_inputs["input_ids"].shape
           lowercase__ :   Optional[int]    					=				common_inputs["decoder_input_ids"].shape[1]
           lowercase__					,   lowercase__ :   List[str]    					=				self.num_attention_heads
           lowercase__ :   Dict    					=				(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase__ :   List[str]    					=				decoder_seq_length + 3
           lowercase__ :   Union[str, Any]    					=				(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           lowercase__ :   Tuple    					=				torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(lowercase_	,						lowercase_  )]	,						dim=1  )

           lowercase__ :   Union[str, Any]    					=				[]
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           lowercase__					,   lowercase__ :   List[str]    					=				self.num_layers
           lowercase__ :   List[Any]    					=				min(lowercase_	,						lowercase_  )
           lowercase__ :   List[Any]    					=				max(lowercase_	,						lowercase_  ) - min_num_layers
           lowercase__ :   int    					=				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

           for _ in range(lowercase_  ):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(lowercase_  ),
                       torch.zeros(lowercase_  ),
                       torch.zeros(lowercase_  ),
                       torch.zeros(lowercase_  ),
                   )  )
           # TODO: test this.
           lowercase__ :   str    					=				encoder_shape if remaining_side_name == "encoder" else decoder_shape
           for _ in range(lowercase_	,						lowercase_  ):
               common_inputs["past_key_values"].append((torch.zeros(lowercase_  ), torch.zeros(lowercase_  ))  )
       return common_inputs






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     PreTrainedTokenizer	,						lowercase_				:     int = -1	,						lowercase_				:     int = -1	,						lowercase_				:     bool = False	,						lowercase_				:     Optional[TensorType] = None	,						)					->						Mapping[str, Any]:
       lowercase__ :   Any    					=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )

       if self.use_past:
           if not is_torch_available():
               raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
           else:
               import torch
           lowercase__					,   lowercase__ :   str    					=				common_inputs["input_ids"].shape
           # Not using the same length for past_key_values
           lowercase__ :   Dict    					=				seqlen + 2
           lowercase__					,   lowercase__ :   List[str]    					=				self.num_layers
           lowercase__					,   lowercase__ :   Optional[Any]    					=				self.num_attention_heads
           lowercase__ :   Optional[int]    					=				(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           lowercase__ :   Optional[int]    					=				common_inputs["attention_mask"].dtype
           lowercase__ :   List[Any]    					=				torch.cat(
               [common_inputs["attention_mask"], torch.ones(lowercase_	,						lowercase_	,						dtype=lowercase_  )]	,						dim=1  )
           lowercase__ :   Dict    					=				[
               (torch.zeros(lowercase_  ), torch.zeros(lowercase_  )) for _ in range(lowercase_  )
           ]
       return common_inputs






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     PreTrainedTokenizer	,						lowercase_				:     int = -1	,						lowercase_				:     int = -1	,						lowercase_				:     bool = False	,						lowercase_				:     Optional[TensorType] = None	,						)					->						Mapping[str, Any]:
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       lowercase__ :   List[Any]    					=				compute_effective_axis_dimension(
           lowercase_	,						fixed_dimension=OnnxConfig.default_fixed_batch	,						num_token_to_add=0  )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       lowercase__ :   Optional[Any]    					=				tokenizer.num_special_tokens_to_add(lowercase_  )
       lowercase__ :   List[Any]    					=				compute_effective_axis_dimension(
           lowercase_	,						fixed_dimension=OnnxConfig.default_fixed_sequence	,						num_token_to_add=lowercase_  )

       # Generate dummy inputs according to compute batch and sequence
       lowercase__ :   int    					=				[" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
       lowercase__ :   Union[str, Any]    					=				dict(tokenizer(lowercase_	,						return_tensors=lowercase_  )  )
       return common_inputs






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     PreTrainedTokenizer	,						lowercase_				:     int = -1	,						lowercase_				:     int = -1	,						lowercase_				:     bool = False	,						lowercase_				:     Optional[TensorType] = None	,						)					->						Mapping[str, Any]:
       if self.task in ["default", "seq2seq-lm"]:
           lowercase__ :   str    					=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               lowercase_	,						batch_size=lowercase_	,						seq_length=lowercase_	,						is_pair=lowercase_	,						framework=lowercase_  )

       elif self.task == "causal-lm":
           lowercase__ :   List[str]    					=				self._generate_dummy_inputs_for_causal_lm(
               lowercase_	,						batch_size=lowercase_	,						seq_length=lowercase_	,						is_pair=lowercase_	,						framework=lowercase_  )
       else:
           lowercase__ :   Optional[Any]    					=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase_	,						batch_size=lowercase_	,						seq_length=lowercase_	,						is_pair=lowercase_	,						framework=lowercase_  )

       return common_inputs







   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Tuple	,						lowercase_				:     str	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     List[Any]  )					->						Any:
       if self.task in ["default", "seq2seq-lm"]:
           lowercase__ :   Dict    					=				super()._flatten_past_key_values_(lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )
       else:
           lowercase__ :   str    					=				super(lowercase_	,						self  )._flatten_past_key_values_(
               lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )




'''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,
         }




