


"""simple docstring"""

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import DeformableDetrImageProcessor





class    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	Optional[int]      ,       A_	:	List[Any]      ,       A_	:	Any=7      ,       A_	:	Tuple=3      ,       A_	:	str=3_0      ,       A_	:	int=4_0_0      ,       A_	:	Optional[Any]=True      ,       A_	:	Union[str, Any]=None      ,       A_	:	List[str]=True      ,       A_	:	Optional[int]=[0.5, 0.5, 0.5]      ,       A_	:	Optional[int]=[0.5, 0.5, 0.5]      ,       A_	:	Union[str, Any]=True      ,       A_	:	Optional[int]=1 / 2_5_5      ,       A_	:	Optional[Any]=True      ,       ):






       '''simple docstring'''


       __lowercase									=	size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	min_resolution
       __lowercase									=	max_resolution
       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	do_normalize
       __lowercase									=	image_mean
       __lowercase									=	image_std
       __lowercase									=	do_rescale
       __lowercase									=	rescale_factor
       __lowercase									=	do_pad
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }






      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	List[str]      ,       A_	:	List[Any]=False							):






       '''simple docstring'''


       if not batched:
        __lowercase									=	image_inputs[0]
        if isinstance(A_      ,       Image.Image							):
         __lowercase							,		__lowercase									=	image.size
        else:
         __lowercase							,		__lowercase									=	image.shape[1], image.shape[2]
        if w < h:
         __lowercase									=	int(self.size["""shortest_edge"""] * h / w							)
         __lowercase									=	self.size["""shortest_edge"""]
        elif w > h:
         __lowercase									=	self.size["""shortest_edge"""]
         __lowercase									=	int(self.size["""shortest_edge"""] * w / h							)
        else:
         __lowercase									=	self.size["""shortest_edge"""]
         __lowercase									=	self.size["""shortest_edge"""]

       else:
        __lowercase									=	[]
        for image in image_inputs:
         __lowercase							,		__lowercase									=	self.get_expected_values([image]							)
         expected_values.append((expected_height, expected_width)							)
        __lowercase									=	max(A_      ,       key=lambda A_							: item[0]							)[0]
        __lowercase									=	max(A_      ,       key=lambda A_							: item[1]							)[1]

       return expected_height, expected_width






@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Dict											=		DeformableDetrImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	DeformableDetrImageProcessingTester(self							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(A_      ,       """image_mean"""							)							)
       self.assertTrue(hasattr(A_      ,       """image_std"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_normalize"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_resize"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_rescale"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_pad"""							)							)
       self.assertTrue(hasattr(A_      ,       """size"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}							)
       self.assertEqual(image_processor.do_pad      ,       A_							)

       __lowercase									=	self.image_processing_class.from_dict(
           self.image_processor_dict      ,       size=4_2      ,       max_size=8_4      ,       pad_and_return_pixel_mask=A_							)
       self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 4_2, """longest_edge""": 8_4}							)
       self.assertEqual(image_processor.do_pad      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       __lowercase									=	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       Image.Image							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_							)

       self.assertEqual(
           encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

       # Test batched
       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_      ,       batched=A_							)

       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values
       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       __lowercase									=	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=A_      ,       numpify=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       np.ndarray							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_							)

       self.assertEqual(
           encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

       # Test batched
       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_      ,       batched=A_							)

       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowercase									=	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=A_      ,       torchify=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       torch.Tensor							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_							)

       self.assertEqual(
           encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

       # Test batched
       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_      ,       batched=A_							)

       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,       )
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,       """r"""							) as f:
        __lowercase									=	json.loads(f.read()							)

       __lowercase									=	{"""image_id""": 3_9_7_6_9, """annotations""": target}

       # encode them
       __lowercase									=	DeformableDetrImageProcessor()
       __lowercase									=	image_processing(images=A_      ,       annotations=A_      ,       return_tensors="""pt"""							)

       # verify pixel values
       __lowercase									=	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
       self.assertEqual(encoding["""pixel_values"""].shape      ,       A_							)

       __lowercase									=	torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       A_      ,       atol=1e-4							)							)

       # verify area
       __lowercase									=	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       A_							)							)
       # verify boxes
       __lowercase									=	torch.Size([6, 4]							)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       A_							)
       __lowercase									=	torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       A_      ,       atol=1e-3							)							)
       # verify image_id
       __lowercase									=	torch.tensor([3_9_7_6_9]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       A_							)							)
       # verify is_crowd
       __lowercase									=	torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       A_							)							)
       # verify class_labels
       __lowercase									=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       A_							)							)
       # verify orig_size
       __lowercase									=	torch.tensor([4_8_0, 6_4_0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       A_							)							)
       # verify size
       __lowercase									=	torch.tensor([8_0_0, 1_0_6_6]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       A_							)							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
       with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,       """r"""							) as f:
        __lowercase									=	json.loads(f.read()							)

       __lowercase									=	{"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

       __lowercase									=	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

       # encode them
       __lowercase									=	DeformableDetrImageProcessor(format="""coco_panoptic"""							)
       __lowercase									=	image_processing(images=A_      ,       annotations=A_      ,       masks_path=A_      ,       return_tensors="""pt"""							)

       # verify pixel values
       __lowercase									=	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
       self.assertEqual(encoding["""pixel_values"""].shape      ,       A_							)

       __lowercase									=	torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       A_      ,       atol=1e-4							)							)

       # verify area
       __lowercase									=	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       A_							)							)
       # verify boxes
       __lowercase									=	torch.Size([6, 4]							)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       A_							)
       __lowercase									=	torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       A_      ,       atol=1e-3							)							)
       # verify image_id
       __lowercase									=	torch.tensor([3_9_7_6_9]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       A_							)							)
       # verify is_crowd
       __lowercase									=	torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       A_							)							)
       # verify class_labels
       __lowercase									=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       A_							)							)
       # verify masks
       __lowercase									=	8_2_2_8_7_3
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       A_							)
       # verify orig_size
       __lowercase									=	torch.tensor([4_8_0, 6_4_0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       A_							)							)
       # verify size
       __lowercase									=	torch.tensor([8_0_0, 1_0_6_6]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       A_							)							)




"""simple docstring"""

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__  							=logging.getLogger(__name__)

if __name__ == "__main__":
       UpperCAmelCase__  							=argparse.ArgumentParser(
           description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
       )
       parser.add_argument(
           "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
       )
       parser.add_argument(
           "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
       )
       parser.add_argument("--vocab_size", default=3_0522, type=int)
       UpperCAmelCase__  							=parser.parse_args()

       logger.info(f"""Loading data from {args.data_file}""")
       with open(args.data_file, "rb") as fp:
              UpperCAmelCase__  							=pickle.load(fp)

       logger.info("Counting occurrences for MLM.")
       UpperCAmelCase__  							=Counter()
       for tk_ids in data:
              counter.update(tk_ids)
       UpperCAmelCase__  							=[0] * args.vocab_size
       for k, v in counter.items():
              UpperCAmelCase__  							=v

       logger.info(f"""Dump to {args.token_counts_dump}""")
       with open(args.token_counts_dump, "wb") as handle:
              pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
