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


_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Tuple     			=						{
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}


class   lowercase      (       a       ):
							lowercase__  :    Any									=							"""yolos"""




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   str=768		,							_UpperCamelCase				:   Any=12		,							_UpperCamelCase				:   int=12		,							_UpperCamelCase				:   List[Any]=3_072		,							_UpperCamelCase				:   Dict="gelu"		,							_UpperCamelCase				:   Any=0.0		,							_UpperCamelCase				:   List[Any]=0.0		,							_UpperCamelCase				:   Optional[Any]=0.0_2		,							_UpperCamelCase				:   Union[str, Any]=1e-12		,							_UpperCamelCase				:   Tuple=[512, 864]		,							_UpperCamelCase				:   Optional[int]=16		,							_UpperCamelCase				:   str=3		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Union[str, Any]=100		,							_UpperCamelCase				:   Optional[Any]=True		,							_UpperCamelCase				:   List[Any]=False		,							_UpperCamelCase				:   List[Any]=1		,							_UpperCamelCase				:   Union[str, Any]=5		,							_UpperCamelCase				:   Union[str, Any]=2		,							_UpperCamelCase				:   Optional[Any]=5		,							_UpperCamelCase				:   Dict=2		,							_UpperCamelCase				:   Tuple=0.1		,							**_UpperCamelCase				:   int		,							)   ->			Union[str, Any]:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    intermediate_size
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
												SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    layer_norm_eps
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    patch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    qkv_bias
												SCREAMING_SNAKE_CASE           =    num_detection_tokens
												SCREAMING_SNAKE_CASE           =    use_mid_position_embeddings
												SCREAMING_SNAKE_CASE           =    auxiliary_loss
												# Hungarian matcher
												SCREAMING_SNAKE_CASE           =    class_cost
												SCREAMING_SNAKE_CASE           =    bbox_cost
												SCREAMING_SNAKE_CASE           =    giou_cost
												# Loss coefficients
												SCREAMING_SNAKE_CASE           =    bbox_loss_coefficient
												SCREAMING_SNAKE_CASE           =    giou_loss_coefficient
												SCREAMING_SNAKE_CASE           =    eos_coefficient



class   lowercase      (       a       ):
							lowercase__  :    Union[str, Any]									=							version.parse("""1.11"""       )




							@property
							def 		__snake_case(  self				:   int )   ->			Mapping[str, Mapping[int, str]]:



												'''simple docstring'''

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




							@property
							def 		__snake_case(  self				:   str )   ->			float:



												'''simple docstring'''

												return 1e-4





							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			int:



												'''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 numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
							from PIL import Image


class   lowercase      (       a       ):
							lowercase__  :    Optional[Any]									=							(
							    """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
							    """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
							    """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
							)
							lowercase__  :    Optional[int]									=							"""CIDAS/clipseg-rd64-refined"""
							lowercase__  :    Tuple									=							"""image_segmenter"""
							lowercase__  :    Optional[Any]									=							CLIPSegForImageSegmentation

							lowercase__  :    int									=							["""image""", """text"""]
							lowercase__  :    List[str]									=							["""image"""]




							def __init__(  self				:   str		,							*_UpperCamelCase				:   str		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												requires_backends(self		,							["vision"] )
												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   "Image"		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return self.pre_processor(text=[label]		,							images=[image]		,							padding=_UpperCamelCase		,							return_tensors="pt" )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    self.model(**_UpperCamelCase ).logits
												return logits





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    outputs.cpu().detach().numpy()
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    1
												return Image.fromarray((array * 255).astype(np.uinta ) )




