


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					str					   =    [R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
					@register_to_config
					def __init__(   self     :Dict    ,__snake_case     :int    ,__snake_case     :int    ,__snake_case     :Optional[int] = None    ,__snake_case     :int = 5_02_57    ,__snake_case     :int = 10_24    ,__snake_case     :int = 7_68    ,__snake_case     :int = 12    ,__snake_case     :int = 12    ,__snake_case     :Optional[int] = None    ,__snake_case     :str = "gelu_new"    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 1E-5    ,__snake_case     :float = 0.02    ,__snake_case     :bool = True    ,__snake_case     :bool = True    ,__snake_case     :bool = False    ,__snake_case     :bool = False    ,)      ->					int:
												super().__init__()

												a__    =  prefix_length

												if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																			raise ValueError(
																			    F'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
																			    F' `n_embd`: {n_embd} are not equal.'   )

												a__    =  prefix_inner_dim
												a__    =  prefix_hidden_dim

												a__    =  (
												    nn.Linear(self.prefix_inner_dim    ,self.prefix_hidden_dim   )
												    if self.prefix_hidden_dim is not None
												    else nn.Identity()
												)
												a__    =  (
												    nn.Linear(self.prefix_hidden_dim    ,__snake_case   ) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												a__    =  GPTaConfig(
												    vocab_size=__snake_case    ,n_positions=__snake_case    ,n_embd=__snake_case    ,n_layer=__snake_case    ,n_head=__snake_case    ,n_inner=__snake_case    ,activation_function=__snake_case    ,resid_pdrop=__snake_case    ,embd_pdrop=__snake_case    ,attn_pdrop=__snake_case    ,layer_norm_epsilon=__snake_case    ,initializer_range=__snake_case    ,scale_attn_weights=__snake_case    ,use_cache=__snake_case    ,scale_attn_by_inverse_layer_idx=__snake_case    ,reorder_and_upcast_attn=__snake_case    ,)
												a__    =  GPTaLMHeadModel(__snake_case   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor    ,__snake_case     :Optional[torch.Tensor] = None    ,__snake_case     :Optional[torch.Tensor] = None    ,)      ->					Union[str, Any]:
												a__    =  self.transformer.transformer.wte(__snake_case   )
												a__    =  self.encode_prefix(__snake_case   )
												a__    =  self.decode_prefix(__snake_case   )
												a__    =  torch.cat((prefix_embeds, embedding_text)    ,dim=1   )

												if labels is not None:
																			a__    =  self.get_dummy_token(input_ids.shape[0]    ,input_ids.device   )
																			a__    =  torch.cat((dummy_token, input_ids)    ,dim=1   )
												a__    =  self.transformer(inputs_embeds=__snake_case    ,labels=__snake_case    ,attention_mask=__snake_case   )
												if self.prefix_hidden_dim is not None:
																			return out, hidden
												else:
																			return out
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :int    ,__snake_case     :torch.device   )      ->					torch.Tensor:
												return torch.zeros(__snake_case    ,self.prefix_length    ,dtype=torch.intaa    ,device=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Optional[Any]   )      ->					Tuple:
												return self.encode_prefix(__snake_case   )
					@torch.no_grad()
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Tuple    ,__snake_case     :str    ,__snake_case     :Any   )      ->					Dict:
												a__    =  torch.split(__snake_case    ,1    ,dim=0   )
												a__    =  []
												a__    =  []
												for feature in features:
																			a__    =  self.decode_prefix(feature.to(__snake_case   )   )  # back to the clip feature
																			# Only support beam search for now
																			a__   ,      a__    =  self.generate_beam(
																			    input_embeds=__snake_case    ,device=__snake_case    ,eos_token_id=__snake_case   )
																			generated_tokens.append(output_tokens[0]   )
																			generated_seq_lengths.append(seq_lengths[0]   )
												a__    =  torch.stack(__snake_case   )
												a__    =  torch.stack(__snake_case   )
												return generated_tokens, generated_seq_lengths



					@torch.no_grad()
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :List[Any]=None    ,__snake_case     :Tuple=None    ,__snake_case     :Optional[int]=None    ,__snake_case     :int = 5    ,__snake_case     :int = 67    ,__snake_case     :float = 1.0    ,__snake_case     :Optional[int] = None    ,)      ->					List[str]:
												a__    =  eos_token_id
												a__    =  None
												a__    =  None
												a__    =  torch.ones(__snake_case    ,device=__snake_case    ,dtype=torch.int   )
												a__    =  torch.zeros(__snake_case    ,device=__snake_case    ,dtype=torch.bool   )

												if input_embeds is not None:
																			a__    =  input_embeds
												else:
																			a__    =  self.transformer.transformer.wte(__snake_case   )

												for i in range(__snake_case   ):
																			a__    =  self.transformer(inputs_embeds=__snake_case   )
																			a__    =  outputs.logits
																			a__    =  logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																			a__    =  logits.softmax(-1   ).log()

																			if scores is None:
																										a__   ,      a__    =  logits.topk(__snake_case    ,-1   )
																										a__    =  generated.expand(__snake_case    ,*generated.shape[1:]   )
																										a__   ,      a__    =  next_tokens.permute(1    ,0   ), scores.squeeze(0   )
																										if tokens is None:
																																	a__    =  next_tokens
																										else:
																																	a__    =  tokens.expand(__snake_case    ,*tokens.shape[1:]   )
																																	a__    =  torch.cat((tokens, next_tokens)    ,dim=1   )
																			else:
																										a__    =  -float(np.inf   )
																										a__    =  0
																										a__    =  scores[:, None] + logits
																										seq_lengths[~is_stopped] += 1
																										a__    =  scores_sum / seq_lengths[:, None]
																										a__   ,      a__    =  scores_sum_average.view(-1   ).topk(__snake_case    ,-1   )
																										a__    =  next_tokens // scores_sum.shape[1]
																										a__    =  seq_lengths[next_tokens_source]
																										a__    =  next_tokens % scores_sum.shape[1]
																										a__    =  next_tokens.unsqueeze(1   )
																										a__    =  tokens[next_tokens_source]
																										a__    =  torch.cat((tokens, next_tokens)    ,dim=1   )
																										a__    =  generated[next_tokens_source]
																										a__    =  scores_sum_average * seq_lengths
																										a__    =  is_stopped[next_tokens_source]

																			a__    =  self.transformer.transformer.wte(next_tokens.squeeze()   ).view(generated.shape[0]    ,1    ,-1   )
																			a__    =  torch.cat((generated, next_token_embed)    ,dim=1   )
																			a__    =  is_stopped + next_tokens.eq(__snake_case   ).squeeze()
																			if is_stopped.all():
																										break

												a__    =  scores / seq_lengths
												a__    =  scores.argsort(descending=__snake_case   )
												# tokens tensors are already padded to max_seq_length
												a__    =  [tokens[i] for i in order]
												a__    =  torch.stack(__snake_case    ,dim=0   )
												a__    =  torch.tensor([seq_lengths[i] for i in order]    ,dtype=seq_lengths.dtype   )
												return output_texts, seq_lengths








from sklearn.metrics import fa_score

import datasets


snake_case							: Optional[int]      =   '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


snake_case							: List[Any]      =   '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


snake_case							: Union[str, Any]      =   '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,		_KWARGS_DESCRIPTION   )
class  snake_case_     (datasets.Metric   ):
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												return datasets.MetricInfo(
												    description=_DESCRIPTION    ,citation=_CITATION    ,inputs_description=_KWARGS_DESCRIPTION    ,features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('int32'   )   ),
												            'references': datasets.Sequence(datasets.Value('int32'   )   ),
												        }
												        if self.config_name == 'multilabel'
												        else {
												            'predictions': datasets.Value('int32'   ),
												            'references': datasets.Value('int32'   ),
												        }   )    ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']    ,)



					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Dict=None    ,__snake_case     :str=1    ,__snake_case     :Optional[int]="binary"    ,__snake_case     :Union[str, Any]=None   )      ->					Tuple:
												a__    =  fa_score(
												    __snake_case    ,__snake_case    ,labels=__snake_case    ,pos_label=__snake_case    ,average=__snake_case    ,sample_weight=__snake_case   )
												return {"f1": float(__snake_case   ) if score.size == 1 else score}




