




"""simple docstring"""







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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
		from ...onnx.config import PatchingSpec
		from ...tokenization_utils_base import PreTrainedTokenizerBase


a            =							logging.get_logger(__name__)

a            =							{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 					SCREAMING_SNAKE_CASE__    (    lowercase_    ):
						_a			  =  'longformer'


						def __init__(							self :	Union[str, Any]  ,  lowerCAmelCase :	Union[List[int], int] = 512  ,  lowerCAmelCase :	int = 2  ,  lowerCAmelCase :	int = 1  ,  lowerCAmelCase :	int = 0  ,  lowerCAmelCase :	int = 2  ,  lowerCAmelCase :	int = 3_0522  ,  lowerCAmelCase :	int = 768  ,  lowerCAmelCase :	int = 12  ,  lowerCAmelCase :	int = 12  ,  lowerCAmelCase :	int = 3072  ,  lowerCAmelCase :	str = "gelu"  ,  lowerCAmelCase :	float = 0.1  ,  lowerCAmelCase :	float = 0.1  ,  lowerCAmelCase :	int = 512  ,  lowerCAmelCase :	int = 2  ,  lowerCAmelCase :	float = 0.02  ,  lowerCAmelCase :	float = 1e-12  ,  lowerCAmelCase :	bool = False  ,  **lowerCAmelCase :	List[str]  ,  ):
										super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )

										lowerCAmelCase              = attention_window
										lowerCAmelCase              = sep_token_id
										lowerCAmelCase              = bos_token_id
										lowerCAmelCase              = eos_token_id
										lowerCAmelCase              = vocab_size
										lowerCAmelCase              = hidden_size
										lowerCAmelCase              = num_hidden_layers
										lowerCAmelCase              = num_attention_heads
										lowerCAmelCase              = hidden_act
										lowerCAmelCase              = intermediate_size
										lowerCAmelCase              = hidden_dropout_prob
										lowerCAmelCase              = attention_probs_dropout_prob
										lowerCAmelCase              = max_position_embeddings
										lowerCAmelCase              = type_vocab_size
										lowerCAmelCase              = initializer_range
										lowerCAmelCase              = layer_norm_eps
										lowerCAmelCase              = onnx_export






class 					SCREAMING_SNAKE_CASE__    (    lowercase_    ):


						def __init__(							self :	List[Any]  ,  lowerCAmelCase :	"PretrainedConfig"  ,  lowerCAmelCase :	str = "default"  ,  lowerCAmelCase :	"List[PatchingSpec]" = None    ):
										super().__init__(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = True


						@property
						def      __lowercase       (							self :	Dict    ):
										if self.task == "multiple-choice":
														lowerCAmelCase              = {0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
														lowerCAmelCase              = {0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""global_attention_mask""", dynamic_axis),
										    ]    )


						@property
						def      __lowercase       (							self :	int    ):
										lowerCAmelCase              = super().outputs
										if self.task == "default":
														lowerCAmelCase              = {0: 'batch'}
										return outputs


						@property
						def      __lowercase       (							self :	Dict    ):
										return 1e-4


						@property
						def      __lowercase       (							self :	Union[str, Any]    ):
										# needs to be >= 14 to support tril operator
										return max(super().default_onnx_opset  ,  14    )




						def      __lowercase       (							self :	Tuple  ,  lowerCAmelCase :	"PreTrainedTokenizerBase"  ,  lowerCAmelCase :	int = -1  ,  lowerCAmelCase :	int = -1  ,  lowerCAmelCase :	bool = False  ,  lowerCAmelCase :	Optional[TensorType] = None  ,  ):
										lowerCAmelCase              = super().generate_dummy_inputs(
										    preprocessor=SCREAMING_SNAKE_CASE_  ,  batch_size=SCREAMING_SNAKE_CASE_  ,  seq_length=SCREAMING_SNAKE_CASE_  ,  is_pair=SCREAMING_SNAKE_CASE_  ,  framework=SCREAMING_SNAKE_CASE_    )
										import torch

										# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
										# makes the export fail randomly
										lowerCAmelCase              = torch.zeros_like(inputs["""input_ids"""]    )
										# make every second token global
										lowerCAmelCase              = 1

										return inputs










"""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 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       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (
          """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."""
      )
      _SCREAMING_SNAKE_CASE  				=     """CIDAS/clipseg-rd64-refined"""
      _SCREAMING_SNAKE_CASE  				=     """image_segmenter"""
      _SCREAMING_SNAKE_CASE  				=     CLIPSegForImageSegmentation

      _SCREAMING_SNAKE_CASE  				=     ["""image""", """text"""]
      _SCREAMING_SNAKE_CASE  				=     ["""image"""]



      def __init__(      self				:      Dict    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        requires_backends(self    ,			['vision']       )
        super().__init__(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      "Image"    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        return self.pre_processor(text=[label]    ,			images=[image]    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        with torch.no_grad():
          lowerCAmelCase_   :				List[str]								= self.model(**SCREAMING_SNAKE_CASE_       ).logits
        return logits





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= outputs.cpu().detach().numpy()
        lowerCAmelCase_   :				Optional[Any]								= 0
        lowerCAmelCase_   :				Optional[Any]								= 1
        return Image.fromarray((array * 2_5_5).astype(np.uinta       )       )





