



"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """facebook/bart-large-mnli"""
			__magic_name__     					=     (
			    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
			    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
			    """It returns the most likely label in the list of provided `labels` for the input text."""
			)
			__magic_name__     					=     """text_classifier"""
			__magic_name__     					=     AutoTokenizer
			__magic_name__     					=     AutoModelForSequenceClassification

			__magic_name__     					=     ["""text""", ["""text"""]]
			__magic_name__     					=     ["""text"""]



			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[Any]:
									super().setup()
									snake_case   :			List[Any]						    = self.model.config
									snake_case   :			Any						    = -1
									for idx, label in config.idalabel.items():
															if label.lower().startswith("entail"		):
																					snake_case   :			List[str]						    = int(_lowercase		)
									if self.entailment_id == -1:
															raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				Any   ,	_lowercase							:				int		)							->					str:
									snake_case   :			List[str]						    = labels
									return self.pre_processor(
									    [text] * len(_lowercase		)   ,	[F'''This example is {label}''' for label in labels]   ,	return_tensors="pt"   ,	padding="max_length"   ,	)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				Union[str, Any]		)							->					int:
									snake_case   :			Tuple						    = outputs.logits
									snake_case   :			Union[str, Any]						    = torch.argmax(logits[:, 2]		).item()
									return self._labels[label_id]








"""simple docstring"""







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

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



