






from sklearn.metrics import fa_score

import datasets


__magic_name__:					str							=     "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


__magic_name__:					List[str]							=     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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'`.\n\n        - '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.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - '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.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> 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])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


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





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION				)
class 	snake_case__ (							datasets.Metric				):
	def       __magic_name__    (    self			)							->				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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__="binary"      ,       lowerCAmelCase__=None			)							->				Any:
							__magic_name__      :       int       						= fa_score(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       labels=lowerCAmelCase__      ,       pos_label=lowerCAmelCase__      ,       average=lowerCAmelCase__      ,       sample_weight=lowerCAmelCase__			)
							return {"f1": float(lowerCAmelCase__			) if score.size == 1 else score}








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


__magic_name__:					Any							=     logging.get_logger(__name__)

__magic_name__:					Dict							=     {
    "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__ (							_lowerCAmelCase				):
	lowercase__						:					Any											=   '''blenderbot-small'''
	lowercase__						:					Optional[int]											=   ['''past_key_values''']
	lowercase__						:					Dict											=   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
	def __init__(    self      ,       lowerCAmelCase__=5_02_65      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=2      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							__magic_name__      :       Tuple       						= vocab_size
							__magic_name__      :       List[str]       						= max_position_embeddings
							__magic_name__      :       Union[str, Any]       						= d_model
							__magic_name__      :       Optional[int]       						= encoder_ffn_dim
							__magic_name__      :       Union[str, Any]       						= encoder_layers
							__magic_name__      :       List[str]       						= encoder_attention_heads
							__magic_name__      :       List[Any]       						= decoder_ffn_dim
							__magic_name__      :       str       						= decoder_layers
							__magic_name__      :       List[str]       						= decoder_attention_heads
							__magic_name__      :       Union[str, Any]       						= dropout
							__magic_name__      :       Tuple       						= attention_dropout
							__magic_name__      :       List[Any]       						= activation_dropout
							__magic_name__      :       List[Any]       						= activation_function
							__magic_name__      :       Optional[int]       						= init_std
							__magic_name__      :       Dict       						= encoder_layerdrop
							__magic_name__      :       Union[str, Any]       						= decoder_layerdrop
							__magic_name__      :       Optional[int]       						= use_cache
							__magic_name__      :       List[Any]       						= encoder_layers
							__magic_name__      :       Dict       						= scale_embedding  # scale factor will be sqrt(d_model) if True

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





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

													if self.use_past:
																			__magic_name__      :       List[Any]       						= {0: """batch"""}
																			__magic_name__      :       Dict       						= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
													else:
																			__magic_name__      :       List[str]       						= {0: """batch""", 1: """decoder_sequence"""}
																			__magic_name__      :       Any       						= {0: """batch""", 1: """decoder_sequence"""}

													if self.use_past:
																			self.fill_with_past_key_values_(lowerCAmelCase__      ,       direction="""inputs"""			)
							elif self.task == "causal-lm":
													# TODO: figure this case out.
													__magic_name__      :       Union[str, Any]       						= OrderedDict(
													    [
													        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
													        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
													    ]			)
													if self.use_past:
																			__magic_name__						,__magic_name__      :       Dict       						= self.num_layers
																			for i in range(lowerCAmelCase__			):
																									__magic_name__      :       Dict       						= {0: """batch""", 2: """past_sequence + sequence"""}
																									__magic_name__      :       int       						= {0: """batch""", 2: """past_sequence + sequence"""}
							else:
													__magic_name__      :       Union[str, Any]       						= 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       __magic_name__    (    self			)							->				Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Any       						= super().outputs
							else:
													__magic_name__      :       int       						= super(lowerCAmelCase__      ,       self			).outputs
													if self.use_past:
																			__magic_name__						,__magic_name__      :       str       						= self.num_layers
																			for i in range(lowerCAmelCase__			):
																									__magic_name__      :       Tuple       						= {0: """batch""", 2: """past_sequence + sequence"""}
																									__magic_name__      :       Dict       						= {0: """batch""", 2: """past_sequence + sequence"""}
							return common_outputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							__magic_name__      :       Tuple       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

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

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

													__magic_name__      :       List[str]       						= torch.cat(
													    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase__      ,       lowerCAmelCase__			)]      ,       dim=1			)

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

													for _ in range(lowerCAmelCase__			):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(lowerCAmelCase__			),
																			        torch.zeros(lowerCAmelCase__			),
																			        torch.zeros(lowerCAmelCase__			),
																			        torch.zeros(lowerCAmelCase__			),
																			    )			)
													# TODO: test this.
													__magic_name__      :       Union[str, Any]       						= encoder_shape if remaining_side_name == """encoder""" else decoder_shape
													for _ in range(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__			), torch.zeros(lowerCAmelCase__			))			)
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							__magic_name__      :       int       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

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

													__magic_name__      :       Optional[int]       						= common_inputs["""attention_mask"""].dtype
													__magic_name__      :       Optional[Any]       						= torch.cat(
													    [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase__      ,       lowerCAmelCase__      ,       dtype=lowerCAmelCase__			)]      ,       dim=1			)
													__magic_name__      :       Tuple       						= [
													    (torch.zeros(lowerCAmelCase__			), torch.zeros(lowerCAmelCase__			)) for _ in range(lowerCAmelCase__			)
													]
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = 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
							__magic_name__      :       Tuple       						= compute_effective_axis_dimension(
							    lowerCAmelCase__      ,       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
							__magic_name__      :       str       						= tokenizer.num_special_tokens_to_add(lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= compute_effective_axis_dimension(
							    lowerCAmelCase__      ,       fixed_dimension=OnnxConfig.default_fixed_sequence      ,       num_token_to_add=lowerCAmelCase__			)

							# Generate dummy inputs according to compute batch and sequence
							__magic_name__      :       List[Any]       						= [""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
							__magic_name__      :       List[str]       						= dict(tokenizer(lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__			)			)
							return common_inputs
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = -1      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       )							->				Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       Tuple       						= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)

							elif self.task == "causal-lm":
													__magic_name__      :       str       						= self._generate_dummy_inputs_for_causal_lm(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)
							else:
													__magic_name__      :       Dict       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       seq_length=lowerCAmelCase__      ,       is_pair=lowerCAmelCase__      ,       framework=lowerCAmelCase__			)

							return common_inputs







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Dict:
							if self.task in ["default", "seq2seq-lm"]:
													__magic_name__      :       List[Any]       						= super()._flatten_past_key_values_(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
							else:
													__magic_name__      :       Tuple       						= super(lowerCAmelCase__      ,       self			)._flatten_past_key_values_(
													    lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)
