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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase						:      Union[str, Any]									= logging.get_logger(__name__)

lowercase						:      Union[str, Any]									= {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'gpt_neo'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







	def __init__(      self	:str  ,     a	:Any=5_0_2_5_7  ,     a	:Union[str, Any]=2_0_4_8  ,     a	:Optional[Any]=2_0_4_8  ,     a	:Dict=2_4  ,     a	:Dict=[[["global", "local"], 1_2]]  ,     a	:List[str]=1_6  ,     a	:Optional[Any]=None  ,     a	:Optional[Any]=2_5_6  ,     a	:List[Any]="gelu_new"  ,     a	:str=0.0  ,     a	:Optional[Any]=0.0  ,     a	:Any=0.0  ,     a	:List[str]=0.1  ,     a	:Optional[Any]=1E-5  ,     a	:List[str]=0.02  ,     a	:Optional[int]=True  ,     a	:Dict=5_0_2_5_6  ,     a	:str=5_0_2_5_6  ,     **a	:List[Any]  ,     )	->				Optional[Any]:
								__UpperCamelCase			:     Optional[int]         =      vocab_size
								__UpperCamelCase			:     Tuple         =      max_position_embeddings
								__UpperCamelCase			:     str         =      hidden_size
								__UpperCamelCase			:     Optional[Any]         =      num_layers
								__UpperCamelCase			:     Union[str, Any]         =      num_heads
								__UpperCamelCase			:     str         =      intermediate_size
								__UpperCamelCase			:     Any         =      window_size
								__UpperCamelCase			:     Union[str, Any]         =      activation_function
								__UpperCamelCase			:     int         =      resid_dropout
								__UpperCamelCase			:     Optional[int]         =      embed_dropout
								__UpperCamelCase			:     List[str]         =      attention_dropout
								__UpperCamelCase			:     Dict         =      classifier_dropout
								__UpperCamelCase			:     Optional[Any]         =      layer_norm_epsilon
								__UpperCamelCase			:     List[Any]         =      initializer_range
								__UpperCamelCase			:     Any         =      use_cache

								__UpperCamelCase			:     int         =      bos_token_id
								__UpperCamelCase			:     Any         =      eos_token_id

								__UpperCamelCase			:     List[str]         =      attention_types
								__UpperCamelCase			:     Tuple         =      self.expand_attention_types_params(a		)

								if len(self.attention_layers		) != self.num_layers:
															raise ValueError(
															    "Configuration for convolutional module is incorrect. "
															    "It is required that `len(config.attention_layers)` == `config.num_layers` "
															    f'but is `len(config.attention_layers) = {len(self.attention_layers		)}`, '
															    f'`config.num_layers = {self.num_layers}`. '
															    "`config.attention_layers` is prepared using `config.attention_types`. "
															    "Please verify the value of `config.attention_types` argument."		)

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







	@staticmethod
	def      _lowerCamelCase       (      a	:List[Any]		)	->				Any:
								__UpperCamelCase			:     str         =      []
								for item in attention_types:
															for _ in range(item[1]		):
																						attentions.extend(item[0]		)
								return attentions




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Any)      ->					int:






							'''simple docstring'''





							import torch

							__UpperCamelCase			:     List[str]         =      input.size()
							__UpperCamelCase			:     int         =      len(_lowerCamelCase)
							__UpperCamelCase			:     Dict         =      shape[dimension]

							__UpperCamelCase			:     Union[str, Any]         =      torch.arange(0		,						_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      torch.div(sizedim - size		,						_lowerCamelCase		,						rounding_mode="floor") + 1
							__UpperCamelCase			:     Dict         =      torch.arange(_lowerCamelCase) + low_indices[:min_length][:, None]

							__UpperCamelCase			:     Dict         =      [slice(_lowerCamelCase)] * rank
							__UpperCamelCase			:     str         =      indices
							__UpperCamelCase			:     Tuple         =      input[s]

							__UpperCamelCase			:     Tuple         =      list(range(0		,						rank + 1))
							perm.append(perm.pop(dimension + 1))

							return sliced.permute(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[Any])      ->					Optional[Any]:






							'''simple docstring'''





							import torch

							__UpperCamelCase			:     List[Any]         =      torch.arange(1		,						_lowerCamelCase)
							__UpperCamelCase			:     Optional[int]         =      torch.remainder(_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      remainders == 0
							__UpperCamelCase			:     Optional[Any]         =      candidates[divisor_indices]
							__UpperCamelCase			:     str         =      torch.max(_lowerCamelCase)
							return largest_divisor, torch.div(_lowerCamelCase		,						_lowerCamelCase		,						rounding_mode="floor")




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	@property
	def      _lowerCamelCase       (      self	:int		)	->				Mapping[str, Mapping[int, str]]:
								__UpperCamelCase			:     Optional[Any]         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
								if self.use_past:
															self.fill_with_past_key_values_(a  ,     direction="inputs"		)
															__UpperCamelCase			:     str         =      {0: "batch", 1: "past_sequence + sequence"}
								else:
															__UpperCamelCase			:     Any         =      {0: "batch", 1: "sequence"}

								return common_inputs







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				int:
								return self._config.num_heads







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

								# We need to order the input in the way they appears in the forward()
								__UpperCamelCase			:     Optional[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

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

								__UpperCamelCase			:     Dict         =      common_inputs["attention_mask"]
								if self.use_past:
															__UpperCamelCase			:     Optional[int]         =      ordered_inputs["attention_mask"].dtype
															__UpperCamelCase			:     Optional[Any]         =      torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(a  ,     a  ,     dtype=a		)]  ,     dim=1		)

								return ordered_inputs







	@property
	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				int:
								return 1_3

# 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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'facebook/bart-large-mnli'
	_A			=  (
	    '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.'
	)
	_A			=  'text_classifier'
	_A			=  AutoTokenizer
	_A			=  AutoModelForSequenceClassification

	_A			=  ['text', ['text']]
	_A			=  ['text']







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







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Dict  ,     a	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      labels
								return self.pre_processor(
								    [text] * len(a		)  ,     [f'This example is {label}' for label in labels]  ,     return_tensors="pt"  ,     padding="max_length"  ,     )







	def      _lowerCamelCase       (      self	:Any  ,     a	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[int]         =      outputs.logits
								__UpperCamelCase			:     Dict         =      torch.argmax(logits[:, 2]		).item()
								return self._labels[label_id]