





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

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


__A							:			Dict         =			Dict[str, Any]
__A							:			Optional[int]         =			List[Prediction]







@add_end_docstrings(__snake_case	)
class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     *snake_case_				,     **snake_case_							):
         super().__init__(*snake_case_				,     **snake_case_							)

         if self.framework == "tf":
             raise ValueError(F"The {self.__class__} is only available in PyTorch."							)

         requires_backends(self				,     'vision'							)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()							)							)




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         _A     =							{}
         if "threshold" in kwargs:
             _A     =							kwargs['threshold']
         return {}, {}, postprocess_kwargs




     def __call__(    self				,     *snake_case_				,     **snake_case_							):
         return super().__call__(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_image(snake_case_							)
         _A     =							torch.IntTensor([[image.height, image.width]]							)
         _A     =							self.image_processor(images=[image]				,     return_tensors='pt'							)
         if self.tokenizer is not None:
             _A     =							self.tokenizer(text=inputs['words']				,     boxes=inputs['boxes']				,     return_tensors='pt'							)
         _A     =							target_size
         return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							model_inputs.pop('target_size'							)
         _A     =							self.model(**snake_case_							)
         _A     =							outputs.__class__({'target_size': target_size, **outputs}							)
         if self.tokenizer is not None:
             _A     =							model_inputs['bbox']
         return model_outputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0.9							):
         _A     =							model_outputs['target_size']
         if self.tokenizer is not None:
             # This is a LayoutLMForTokenClassification variant.
             # The OCR got the boxes and the model classified the words.
             _A,			_A     =							target_size[0].tolist()

             def unnormalize(snake_case_							):
                 return self._get_bounding_box(
                     torch.Tensor(
                         [
                             (width * bbox[0] / 1000),
                             (height * bbox[1] / 1000),
                             (width * bbox[2] / 1000),
                             (height * bbox[3] / 1000),
                         ]							)							)

             _A,			_A     =							model_outputs['logits'].squeeze(0							).softmax(dim=-1							).max(dim=-1							)
             _A     =							[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
             _A     =							[unnormalize(snake_case_							) for bbox in model_outputs['bbox'].squeeze(0							)]
             _A     =							['score', 'label', 'box']
             _A     =							[dict(zip(snake_case_				,     snake_case_							)							) for vals in zip(scores.tolist()				,     snake_case_				,     snake_case_							) if vals[0] > threshold]
         else:
             # This is a regular ForObjectDetectionModel
             _A     =							self.image_processor.post_process_object_detection(snake_case_				,     snake_case_				,     snake_case_							)
             _A     =							raw_annotations[0]
             _A     =							raw_annotation['scores']
             _A     =							raw_annotation['labels']
             _A     =							raw_annotation['boxes']

             _A     =							scores.tolist()
             _A     =							[self.model.config.idalabel[label.item()] for label in labels]
             _A     =							[self._get_bounding_box(snake_case_							) for box in boxes]

             # {"scores": [...], ...} --> [{"score":x, ...}, ...]
             _A     =							['score', 'label', 'box']
             _A     =							[
                 dict(zip(snake_case_				,     snake_case_							)							)
                 for vals in zip(raw_annotation['scores']				,     raw_annotation['labels']				,     raw_annotation['boxes']							)
             ]

         return annotation




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if self.framework != "pt":
             raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'							)
         _A,			_A,			_A,			_A     =							box.int().tolist()
         _A     =							{
             'xmin': xmin,
             'ymin': ymin,
             'xmax': xmax,
             'ymax': ymax,
         }
         return bbox













# 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 re

from ..utils import cached_file


# docstyle-ignore
__a   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


__a   :							Union[str, Any]						= "huggingface-tools/default-prompts"
__a   :							Optional[int]						= {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    if prompt_or_repo_id is None:
        __A       =						DEFAULT_PROMPTS_REPO

    # prompt is considered a repo ID when it does not contain any kind of space
    if re.search("""\\s"""      ,					__lowercase     ) is not None:
        return prompt_or_repo_id

    __A       =						cached_file(
        __lowercase      ,					PROMPT_FILES[mode]      ,					repo_type="""dataset"""      ,					user_agent={"""agent""": agent_name}     )
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""     ) as f:
        return f.read()




