
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE							:Dict										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[int]										=    {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"table-transformer"
	snake_case_    =					["past_key_values"]
	snake_case_    =					{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}






	def __init__(	self :			Optional[int]							,A :			Optional[Any]=True							,A :			Dict=None							,A :			Any=3							,A :			Union[str, Any]=1_00							,A :			Dict=6							,A :			Optional[int]=20_48							,A :			Any=8							,A :			str=6							,A :			List[str]=20_48							,A :			Any=8							,A :			Optional[Any]=0.0							,A :			str=0.0							,A :			Union[str, Any]=True							,A :			Tuple="relu"							,A :			List[str]=2_56							,A :			Union[str, Any]=0.1							,A :			Tuple=0.0							,A :			Any=0.0							,A :			Tuple=0.02							,A :			Optional[Any]=1.0							,A :			Optional[int]=False							,A :			List[str]="sine"							,A :			List[Any]="resnet50"							,A :			List[Any]=True							,A :			Any=False							,A :			Union[str, Any]=1							,A :			Dict=5							,A :			str=2							,A :			str=1							,A :			Union[str, Any]=1							,A :			Optional[Any]=5							,A :			List[Any]=2							,A :			Tuple=0.1							,**A :			Optional[int]							,):
					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."			)
													__A             =			CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
									elif isinstance(A							,A			):
													__A             =			backbone_config.get("model_type"			)
													__A             =			CONFIG_MAPPING[backbone_model_type]
													__A             =			config_class.from_dict(A			)
									# set timm attributes to None
									__A      ,							__A      ,							__A             =			None, None, None

					__A             =			use_timm_backbone
					__A             =			backbone_config
					__A             =			num_channels
					__A             =			num_queries
					__A             =			d_model
					__A             =			encoder_ffn_dim
					__A             =			encoder_layers
					__A             =			encoder_attention_heads
					__A             =			decoder_ffn_dim
					__A             =			decoder_layers
					__A             =			decoder_attention_heads
					__A             =			dropout
					__A             =			attention_dropout
					__A             =			activation_dropout
					__A             =			activation_function
					__A             =			init_std
					__A             =			init_xavier_std
					__A             =			encoder_layerdrop
					__A             =			decoder_layerdrop
					__A             =			encoder_layers
					__A             =			auxiliary_loss
					__A             =			position_embedding_type
					__A             =			backbone
					__A             =			use_pretrained_backbone
					__A             =			dilation
					# Hungarian matcher
					__A             =			class_cost
					__A             =			bbox_cost
					__A             =			giou_cost
					# Loss coefficients
					__A             =			mask_loss_coefficient
					__A             =			dice_loss_coefficient
					__A             =			bbox_loss_coefficient
					__A             =			giou_loss_coefficient
					__A             =			eos_coefficient
					super().__init__(is_encoder_decoder=A							,**A			)






	@property
	def     UpperCamelCase_  (	self :			Any			):
					return self.encoder_attention_heads







	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return self.d_model





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.11"  )






	@property
	def     UpperCamelCase_  (	self :			int			):
					return OrderedDict(
					    [
					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
					        ("pixel_mask", {0: "batch"}),
					    ]			)






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return 1E-5







	@property
	def     UpperCamelCase_  (	self :			str			):
					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 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "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."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			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     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
