

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 YolosImageProcessor



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=7    ,       A__=3    ,       A__=30    ,       A__=400    ,       A__=True    ,       A__=None    ,       A__=True    ,       A__=[0.5, 0.5, 0.5]    ,       A__=[0.5, 0.5, 0.5]    ,       A__=True    ,       A__=1 / 255    ,       A__=True    ,       ):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						A__							:    Tuple        =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
						A__							:    int        =		parent
						A__							:    Dict        =		batch_size
						A__							:    Optional[int]        =		num_channels
						A__							:    Optional[int]        =		min_resolution
						A__							:    Tuple        =		max_resolution
						A__							:    str        =		do_resize
						A__							:    Optional[int]        =		size
						A__							:    int        =		do_normalize
						A__							:    int        =		image_mean
						A__							:    Optional[Any]        =		image_std
						A__							:    Tuple        =		do_rescale
						A__							:    List[str]        =		rescale_factor
						A__							:    Dict        =		do_pad






					def   __A			( self			):
						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   __A			( self    ,       A__    ,       A__=False			):
						if not batched:
							A__							:    List[str]        =		image_inputs[0]
							if isinstance(A__    ,       Image.Image			):
								A__   ,   A__							:    int        =		image.size
							else:
								A__   ,   A__							:    Union[str, Any]        =		image.shape[1], image.shape[2]
							if w < h:
								A__							:    Any        =		int(self.size["""shortest_edge"""] * h / w			)
								A__							:    Optional[Any]        =		self.size["""shortest_edge"""]
							elif w > h:
								A__							:    List[str]        =		self.size["""shortest_edge"""]
								A__							:    List[Any]        =		int(self.size["""shortest_edge"""] * w / h			)
							else:
								A__							:    str        =		self.size["""shortest_edge"""]
								A__							:    Any        =		self.size["""shortest_edge"""]

						else:
							A__							:    Optional[Any]        =		[]
							for image in image_inputs:
								A__   ,   A__							:    int        =		self.get_expected_values([image]			)
								expected_values.append((expected_height, expected_width)			)
							A__							:    Union[str, Any]        =		max(A__    ,       key=lambda A__			: item[0]			)[0]
							A__							:    Optional[int]        =		max(A__    ,       key=lambda A__			: item[1]			)[1]

						return expected_height, expected_width



@require_torch
@require_vision
class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   List[Any]  							=       YolosImageProcessor if is_vision_available() else None






					def   __A			( self			):
						A__							:    List[str]        =		YolosImageProcessingTester(self			)






					@property
					def   __A			( self			):
						return self.image_processor_tester.prepare_image_processor_dict()






					def   __A			( self			):
						A__							:    Tuple        =		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__    ,       """size"""			)			)






					def   __A			( self			):
						A__							:    Optional[Any]        =		self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 18, """longest_edge""": 1333}			)
						self.assertEqual(image_processor.do_pad    ,       A__			)

						A__							:    Optional[Any]        =		self.image_processing_class.from_dict(
						    self.image_processor_dict    ,       size=42    ,       max_size=84    ,       pad_and_return_pixel_mask=A__			)
						self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 42, """longest_edge""": 84}			)
						self.assertEqual(image_processor.do_pad    ,       A__			)






					def   __A			( self			):
						pass






					def   __A			( self			):
						# Initialize image_processing
						A__							:    Union[str, Any]        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						A__							:    Dict        =		prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=A__			)
						for image in image_inputs:
							self.assertIsInstance(A__    ,       Image.Image			)

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

						A__   ,   A__							:    str        =		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
						A__   ,   A__							:    int        =		self.image_processor_tester.get_expected_values(A__    ,       batched=A__			)

						A__							:    Tuple        =		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   __A			( self			):
						# Initialize image_processing
						A__							:    Any        =		self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						A__							:    int        =		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
						A__							:    int        =		image_processing(image_inputs[0]    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    Tuple        =		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
						A__							:    List[str]        =		image_processing(A__    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    int        =		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   __A			( self			):
						# Initialize image_processing
						A__							:    List[Any]        =		self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						A__							:    str        =		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
						A__							:    List[Any]        =		image_processing(image_inputs[0]    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    Any        =		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
						A__							:    Dict        =		image_processing(A__    ,       return_tensors="""pt"""			).pixel_values

						A__   ,   A__							:    Dict        =		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   __A			( self			):
						# Initialize image_processings
						A__							:    int        =		self.image_processing_class(**self.image_processor_dict			)
						A__							:    List[Any]        =		self.image_processing_class(do_resize=A__    ,       do_normalize=A__    ,       do_rescale=A__			)
						# create random PyTorch tensors
						A__							:    Union[str, Any]        =		prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=A__    ,       torchify=A__			)
						for image in image_inputs:
							self.assertIsInstance(A__    ,       torch.Tensor			)

						# Test whether the method "pad" and calling the image processor return the same tensors
						A__							:    Optional[int]        =		image_processing_a.pad(A__    ,       return_tensors="""pt"""			)
						A__							:    Dict        =		image_processing_a(A__    ,       return_tensors="""pt"""			)

						self.assertTrue(
						    torch.allclose(encoded_images_with_method["""pixel_values"""]    ,       encoded_images["""pixel_values"""]    ,       atol=1e-4			)			)






					@slow
					def   __A			( self			):
						# prepare image and target
						A__							:    List[Any]        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,       """r"""			) as f:
							A__							:    List[str]        =		json.loads(f.read()			)

						A__							:    str        =		{"""image_id""": 3_9769, """annotations""": target}

						# encode them
						A__							:    Tuple        =		YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""			)
						A__							:    List[str]        =		image_processing(images=A__    ,       annotations=A__    ,       return_tensors="""pt"""			)

						# verify pixel values
						A__							:    str        =		torch.Size([1, 3, 800, 1066]			)
						self.assertEqual(encoding["""pixel_values"""].shape    ,       A__			)

						A__							:    Union[str, Any]        =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,       A__    ,       atol=1e-4			)			)

						# verify area
						A__							:    Any        =		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
						A__							:    Optional[int]        =		torch.Size([6, 4]			)
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,       A__			)
						A__							:    Optional[int]        =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,       A__    ,       atol=1e-3			)			)
						# verify image_id
						A__							:    List[Any]        =		torch.tensor([3_9769]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,       A__			)			)
						# verify is_crowd
						A__							:    str        =		torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,       A__			)			)
						# verify class_labels
						A__							:    Any        =		torch.tensor([75, 75, 63, 65, 17, 17]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,       A__			)			)
						# verify orig_size
						A__							:    Tuple        =		torch.tensor([480, 640]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,       A__			)			)
						# verify size
						A__							:    Dict        =		torch.tensor([800, 1066]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,       A__			)			)






					@slow
					def   __A			( self			):
						# prepare image, target and masks_path
						A__							:    int        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,       """r"""			) as f:
							A__							:    Union[str, Any]        =		json.loads(f.read()			)

						A__							:    Any        =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

						A__							:    Union[str, Any]        =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""			)

						# encode them
						A__							:    int        =		YolosImageProcessor(format="""coco_panoptic"""			)
						A__							:    List[str]        =		image_processing(images=A__    ,       annotations=A__    ,       masks_path=A__    ,       return_tensors="""pt"""			)

						# verify pixel values
						A__							:    Any        =		torch.Size([1, 3, 800, 1066]			)
						self.assertEqual(encoding["""pixel_values"""].shape    ,       A__			)

						A__							:    Tuple        =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,       A__    ,       atol=1e-4			)			)

						# verify area
						A__							:    Dict        =		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
						A__							:    Optional[int]        =		torch.Size([6, 4]			)
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,       A__			)
						A__							:    Optional[int]        =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,       A__    ,       atol=1e-3			)			)
						# verify image_id
						A__							:    Union[str, Any]        =		torch.tensor([3_9769]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,       A__			)			)
						# verify is_crowd
						A__							:    Dict        =		torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,       A__			)			)
						# verify class_labels
						A__							:    Dict        =		torch.tensor([17, 17, 63, 75, 75, 93]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,       A__			)			)
						# verify masks
						A__							:    Tuple        =		82_2873
						self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,       A__			)
						# verify orig_size
						A__							:    Optional[Any]        =		torch.tensor([480, 640]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,       A__			)			)
						# verify size
						A__							:    Dict        =		torch.tensor([800, 1066]			)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,       A__			)			)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep			) * [0]
						return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



