



'''simple docstring'''





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
 import PIL

# soft dependency
if is_pytesseract_available():
 import pytesseract

SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)




def        lowerCamelCase  (				_snake_case  :						List[str]     ,_snake_case  :						Dict     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    return [
        int(1_000 * (box[0] / width)       ),
        int(1_000 * (box[1] / height)       ),
        int(1_000 * (box[2] / width)       ),
        int(1_000 * (box[3] / height)       ),
    ]




def        lowerCamelCase  (				_snake_case  :						np.ndarray     ,_snake_case  :						Optional[str]     ,_snake_case  :						Optional[str]       ):





    '''simple docstring'''


    lowercase__		       =						to_pil_image(_snake_case       )
    lowercase__       ,       lowercase__		       =						pil_image.size
    lowercase__		       =						pytesseract.image_to_data(_snake_case     ,lang=_snake_case     ,output_type="dict"     ,config=_snake_case       )
    lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						data["text"], data["left"], data["top"], data["width"], data["height"]

    # filter empty words and corresponding coordinates
    lowercase__		       =						[idx for idx, word in enumerate(_snake_case       ) if not word.strip()]
    lowercase__		       =						[word for idx, word in enumerate(_snake_case       ) if idx not in irrelevant_indices]
    lowercase__		       =						[coord for idx, coord in enumerate(_snake_case       ) if idx not in irrelevant_indices]
    lowercase__		       =						[coord for idx, coord in enumerate(_snake_case       ) if idx not in irrelevant_indices]
    lowercase__		       =						[coord for idx, coord in enumerate(_snake_case       ) if idx not in irrelevant_indices]
    lowercase__		       =						[coord for idx, coord in enumerate(_snake_case       ) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    lowercase__		       =						[]
    for x, y, w, h in zip(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       ):
        lowercase__		       =						[x, y, x + w, y + h]
        actual_boxes.append(_snake_case       )

    # finally, normalize the bounding boxes
    lowercase__		       =						[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(_snake_case     ,_snake_case     ,_snake_case       )       )

    assert len(_snake_case       ) == len(_snake_case       ), "Not as many words as there are bounding boxes"

    return words, normalized_boxes



class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[Any]						   =					["pixel_values"]



       def __init__(				self     ,UpperCAmelCase_ = True     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = PILImageResampling.BILINEAR     ,UpperCAmelCase_ = True     ,UpperCAmelCase_ = 1 / 255     ,UpperCAmelCase_ = True     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = True     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = ""     ,**UpperCAmelCase_     ,)  ->							None:
           super().__init__(**UpperCAmelCase_      )
           lowercase__		       =						size if size is not None else {"height": 224, "width": 224}
           lowercase__		       =						get_size_dict(UpperCAmelCase_      )

           lowercase__		       =						do_resize
           lowercase__		       =						size
           lowercase__		       =						resample
           lowercase__		       =						do_rescale
           lowercase__		       =						rescale_value
           lowercase__		       =						do_normalize
           lowercase__		       =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowercase__		       =						image_std if image_std is not None else IMAGENET_STANDARD_STD
           lowercase__		       =						apply_ocr
           lowercase__		       =						ocr_lang
           lowercase__		       =						tesseract_config



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = PILImageResampling.BILINEAR     ,UpperCAmelCase_ = None     ,**UpperCAmelCase_     ,)  ->							np.ndarray:
           lowercase__		       =						get_size_dict(UpperCAmelCase_      )
           if "height" not in size or "width" not in size:
               raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''      )
           lowercase__		       =						(size["height"], size["width"])
           return resize(UpperCAmelCase_     ,size=UpperCAmelCase_     ,resample=UpperCAmelCase_     ,data_format=UpperCAmelCase_     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,**UpperCAmelCase_     ,)  ->							np.ndarray:
           return rescale(UpperCAmelCase_     ,scale=UpperCAmelCase_     ,data_format=UpperCAmelCase_     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,**UpperCAmelCase_     ,)  ->							np.ndarray:
           return normalize(UpperCAmelCase_     ,mean=UpperCAmelCase_     ,std=UpperCAmelCase_     ,data_format=UpperCAmelCase_     ,**UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_=None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = ChannelDimension.FIRST     ,**UpperCAmelCase_     ,)  ->							PIL.Image.Image:
           lowercase__		       =						do_resize if do_resize is not None else self.do_resize
           lowercase__		       =						size if size is not None else self.size
           lowercase__		       =						get_size_dict(UpperCAmelCase_      )
           lowercase__		       =						resample if resample is not None else self.resample
           lowercase__		       =						do_rescale if do_rescale is not None else self.do_rescale
           lowercase__		       =						rescale_factor if rescale_factor is not None else self.rescale_factor
           lowercase__		       =						do_normalize if do_normalize is not None else self.do_normalize
           lowercase__		       =						image_mean if image_mean is not None else self.image_mean
           lowercase__		       =						image_std if image_std is not None else self.image_std
           lowercase__		       =						apply_ocr if apply_ocr is not None else self.apply_ocr
           lowercase__		       =						ocr_lang if ocr_lang is not None else self.ocr_lang
           lowercase__		       =						tesseract_config if tesseract_config is not None else self.tesseract_config

           lowercase__		       =						make_list_of_images(UpperCAmelCase_      )

           if not valid_images(UpperCAmelCase_      ):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."      )

           if do_resize and size is None:
               raise ValueError("Size must be specified if do_resize is True."      )

           if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."      )

           if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("If do_normalize is True, image_mean and image_std must be specified."      )

           # All transformations expect numpy arrays.
           lowercase__		       =						[to_numpy_array(UpperCAmelCase_      ) for image in images]

           # Tesseract OCR to get words + normalized bounding boxes
           if apply_ocr:
               requires_backends(self     ,"pytesseract"      )
               lowercase__		       =						[]
               lowercase__		       =						[]
               for image in images:
                   lowercase__       ,       lowercase__		       =						apply_tesseract(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )
                   words_batch.append(UpperCAmelCase_      )
                   boxes_batch.append(UpperCAmelCase_      )

           if do_resize:
               lowercase__		       =						[self.resize(image=UpperCAmelCase_     ,size=UpperCAmelCase_     ,resample=UpperCAmelCase_      ) for image in images]

           if do_rescale:
               lowercase__		       =						[self.rescale(image=UpperCAmelCase_     ,scale=UpperCAmelCase_      ) for image in images]

           if do_normalize:
               lowercase__		       =						[self.normalize(image=UpperCAmelCase_     ,mean=UpperCAmelCase_     ,std=UpperCAmelCase_      ) for image in images]

           lowercase__		       =						[to_channel_dimension_format(UpperCAmelCase_     ,UpperCAmelCase_      ) for image in images]

           lowercase__		       =						BatchFeature(data={"pixel_values": images}     ,tensor_type=UpperCAmelCase_      )

           if apply_ocr:
               lowercase__		       =						words_batch
               lowercase__		       =						boxes_batch
           return data









'''simple docstring'''





import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE__      					=     "1"
SCREAMING_SNAKE_CASE__      					=     "0"
SCREAMING_SNAKE_CASE__      					=     "1"

SCREAMING_SNAKE_CASE__      					=     ort.SessionOptions()
SCREAMING_SNAKE_CASE__      					=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
SCREAMING_SNAKE_CASE__      					=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
SCREAMING_SNAKE_CASE__      					=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE__      					=     ort.RunOptions()

SCREAMING_SNAKE_CASE__      					=     128
SCREAMING_SNAKE_CASE__      					=     1
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
SCREAMING_SNAKE_CASE__      					=     time.time()
SCREAMING_SNAKE_CASE__      					=     2000
SCREAMING_SNAKE_CASE__      					=     {}
for iter in range(max_iters):
 SCREAMING_SNAKE_CASE__      					=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))




