






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       List[str]        = '''detr'''
 _lowerCAmelCase       :       Optional[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       Optional[Any]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=3							,   SCREAMING_SNAKE_CASE					:   Any=1_00							,   SCREAMING_SNAKE_CASE					:   str=6							,   SCREAMING_SNAKE_CASE					:   List[str]=20_48							,   SCREAMING_SNAKE_CASE					:   Any=8							,   SCREAMING_SNAKE_CASE					:   Any=6							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Any=8							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Dict=True							,   SCREAMING_SNAKE_CASE					:   str="relu"							,   SCREAMING_SNAKE_CASE					:   List[str]=2_56							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.1							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0_2							,   SCREAMING_SNAKE_CASE					:   List[Any]=1.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Optional[int]="sine"							,   SCREAMING_SNAKE_CASE					:   Optional[int]="resnet50"							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=True							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=False							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   List[str]=5							,   SCREAMING_SNAKE_CASE					:   Optional[int]=2							,   SCREAMING_SNAKE_CASE					:   str=1							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Dict=5							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Tuple=0.1							,   **SCREAMING_SNAKE_CASE					:   str							,   ):


      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."						)

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."						)
                UpperCamelCase__			:   List[str]       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Any       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   int       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Union[str, Any]       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)
           # set timm attributes to None
           UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   str       				=  None, None, None

      UpperCamelCase__			:   Optional[Any]       				=  use_timm_backbone
      UpperCamelCase__			:   Optional[Any]       				=  backbone_config
      UpperCamelCase__			:   Dict       				=  num_channels
      UpperCamelCase__			:   Any       				=  num_queries
      UpperCamelCase__			:   List[str]       				=  d_model
      UpperCamelCase__			:   List[str]       				=  encoder_ffn_dim
      UpperCamelCase__			:   Union[str, Any]       				=  encoder_layers
      UpperCamelCase__			:   Dict       				=  encoder_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  decoder_ffn_dim
      UpperCamelCase__			:   Tuple       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   Union[str, Any]       				=  dropout
      UpperCamelCase__			:   Optional[Any]       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Tuple       				=  activation_function
      UpperCamelCase__			:   List[Any]       				=  init_std
      UpperCamelCase__			:   List[str]       				=  init_xavier_std
      UpperCamelCase__			:   Optional[Any]       				=  encoder_layerdrop
      UpperCamelCase__			:   Any       				=  decoder_layerdrop
      UpperCamelCase__			:   Optional[int]       				=  encoder_layers
      UpperCamelCase__			:   str       				=  auxiliary_loss
      UpperCamelCase__			:   List[Any]       				=  position_embedding_type
      UpperCamelCase__			:   Tuple       				=  backbone
      UpperCamelCase__			:   Tuple       				=  use_pretrained_backbone
      UpperCamelCase__			:   Optional[Any]       				=  dilation
      # Hungarian matcher
      UpperCamelCase__			:   Optional[int]       				=  class_cost
      UpperCamelCase__			:   int       				=  bbox_cost
      UpperCamelCase__			:   Dict       				=  giou_cost
      # Loss coefficients
      UpperCamelCase__			:   Dict       				=  mask_loss_coefficient
      UpperCamelCase__			:   Tuple       				=  dice_loss_coefficient
      UpperCamelCase__			:   Optional[int]       				=  bbox_loss_coefficient
      UpperCamelCase__			:   str       				=  giou_loss_coefficient
      UpperCamelCase__			:   List[str]       				=  eos_coefficient
      super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 @property
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 @classmethod
 def 					__lowercase					(		cls					:   str							,   SCREAMING_SNAKE_CASE					:   PretrainedConfig							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return cls(backbone_config=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  copy.deepcopy(self.__dict__						)
      if output["backbone_config"] is not None:
           UpperCamelCase__			:   int       				=  self.backbone_config.to_dict()
      UpperCamelCase__			:   str       				=  self.__class__.model_type
      return output







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Union[str, Any]        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ("pixel_mask", {0: "batch"}),
          ]						)







 @property
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      return 12







# 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  (    A__       ):
 _lowerCAmelCase       :       str        = '''facebook/bart-large-mnli'''
 _lowerCAmelCase       :       Tuple        = (
     '''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.'''
 )
 _lowerCAmelCase       :       Any        = '''text_classifier'''
 _lowerCAmelCase       :       int        = AutoTokenizer
 _lowerCAmelCase       :       Union[str, Any]        = AutoModelForSequenceClassification

 _lowerCAmelCase       :       Union[str, Any]        = ['''text''', ['''text''']]
 _lowerCAmelCase       :       Dict        = ['''text''']







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__			:   Dict       				=  self.model.config
      UpperCamelCase__			:   Union[str, Any]       				=  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__			:   List[str]       				=  int(SCREAMING_SNAKE_CASE						)
      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					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  labels
      return self.pre_processor(
          [text] * len(SCREAMING_SNAKE_CASE						)							,   [F'This example is {label}' for label in labels]							,   return_tensors="pt"							,   padding="max_length"							,   )







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits
      UpperCamelCase__			:   Any       				=  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]