





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Any    =			{"""vocab_file""": """spiece.model"""}

__magic_name__   :						List[Any]    =			{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

__magic_name__   :						Any    =			{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
__magic_name__   :						Optional[Any]    =			0
__magic_name__   :						str    =			1
__magic_name__   :						str    =			2
__magic_name__   :						Optional[int]    =			3
__magic_name__   :						Any    =			4


class       __SCREAMING_SNAKE_CASE	(						__SCREAMING_SNAKE_CASE			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						Union[str, Any]      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						str      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :						Union[str, Any]      =   'left'

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=False						,   lowerCamelCase=True						,   lowerCamelCase=False						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<sep>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase="<cls>"						,   lowerCamelCase="<mask>"						,   lowerCamelCase=["<eop>", "<eod>"]						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       # Mask token behave like a normal word, i.e. include the space before it
       _snake_case     		=				AddedToken(_a						,   lstrip=_a						,   rstrip=_a	) if isinstance(_a						,   _a	) else mask_token

       _snake_case     		=				{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=_a						,   remove_space=_a						,   keep_accents=_a						,   bos_token=_a						,   eos_token=_a						,   unk_token=_a						,   sep_token=_a						,   pad_token=_a						,   cls_token=_a						,   mask_token=_a						,   additional_special_tokens=_a						,   sp_model_kwargs=self.sp_model_kwargs						,   **_a						,   )

       _snake_case     		=				3

       _snake_case     		=				do_lower_case
       _snake_case     		=				remove_space
       _snake_case     		=				keep_accents
       _snake_case     		=				vocab_file

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(_a	)

     @property
     def 	UpperCamelCase(					self	):
       return len(self.sp_model	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				{self.convert_ids_to_tokens(_a	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab

     def __getstate__(					self	):
       _snake_case     		=				self.__dict__.copy()
       _snake_case     		=				None
       return state

     def __setstate__(					self						,   lowerCamelCase	):
       _snake_case     		=				d

       # for backward compatibility
       if not hasattr(self						,   "sp_model_kwargs"	):
         _snake_case     		=				{}

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(self.vocab_file	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if self.remove_space:
         _snake_case     		=				" ".join(inputs.strip().split()	)
       else:
         _snake_case     		=				inputs
       _snake_case     		=				outputs.replace("``"						,   "\""	).replace("\'\'"						,   "\""	)

       if not self.keep_accents:
         _snake_case     		=				unicodedata.normalize("NFKD"						,   _a	)
         _snake_case     		=				"".join([c for c in outputs if not unicodedata.combining(_a	)]	)
       if self.do_lower_case:
         _snake_case     		=				outputs.lower()

       return outputs

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.preprocess_text(_a	)
       _snake_case     		=				self.sp_model.encode(_a						,   out_type=_a	)
       _snake_case     		=				[]
       for piece in pieces:
         if len(_a	) > 1 and piece[-1] == str(","	) and piece[-2].isdigit():
           _snake_case     		=				self.sp_model.EncodeAsPieces(piece[:-1].replace(_a						,   ""	)	)
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]	) == 1:
               _snake_case     		=				cur_pieces[1:]
             else:
               _snake_case     		=				cur_pieces[0][1:]
           cur_pieces.append(piece[-1]	)
           new_pieces.extend(_a	)
         else:
           new_pieces.append(_a	)

       return new_pieces

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.sp_model.PieceToId(_a	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.sp_model.IdToPiece(_a	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"".join(_a	).replace(_a						,   " "	).strip()
       return out_string

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = False						,   lowerCamelCase = None						,   lowerCamelCase = True						,   **lowerCamelCase						,   ):
       _snake_case     		=				kwargs.pop("use_source_tokenizer"						,   _a	)

       _snake_case     		=				self.convert_ids_to_tokens(_a						,   skip_special_tokens=_a	)

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       _snake_case     		=				[]
       _snake_case     		=				[]
       for token in filtered_tokens:
         if skip_special_tokens and token in self.all_special_ids:
           continue
         if token in self.added_tokens_encoder:
           if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(_a	)	)
             _snake_case     		=				[]
           sub_texts.append(_a	)
         else:
           current_sub_text.append(_a	)
       if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(_a	)	)

       # Mimic the behavior of the Rust tokenizer:
       # By default, there are no spaces between special tokens
       _snake_case     		=				"".join(_a	)

       _snake_case     		=				(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
         _snake_case     		=				self.clean_up_tokenization(_a	)
         return clean_text
       else:
         return text

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[self.sep_token_id]
       _snake_case     		=				[self.cls_token_id]
       if token_ids_a is None:
         return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_a						,   token_ids_a=_a						,   already_has_special_tokens=_a	)

       if token_ids_a is not None:
         return ([0] * len(_a	)) + [1] + ([0] * len(_a	)) + [1, 1]
       return ([0] * len(_a	)) + [1, 1]

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[self.sep_token_id]
       _snake_case     		=				[2]

       if token_ids_a is None:
         return len(token_ids_a + sep	) * [0] + cls_segment_id
       return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       if not os.path.isdir(_a	):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
         return
       _snake_case     		=				os.path.join(
           _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

       if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	) and os.path.isfile(self.vocab_file	):
         copyfile(self.vocab_file						,   _a	)
       elif not os.path.isfile(self.vocab_file	):
         with open(_a						,   "wb"	) as fi:
           _snake_case     		=				self.sp_model.serialized_model_proto()
           fi.write(_a	)

       return (out_vocab_file,)









'''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 DetaImageProcessor


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=True						,   lowerCamelCase=1 / 255						,   lowerCamelCase=True						,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _snake_case     		=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_pad

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

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

       return expected_height, expected_width







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   DetaImageProcessor if is_vision_available() else None

     def 	UpperCamelCase(					self	):
       _snake_case     		=				DetaImageProcessingTester(self	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_mean"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_std"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_normalize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_resize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_rescale"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_pad"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "size"	)	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size						,   {"shortest_edge": 18, "longest_edge": 1_333}	)
       self.assertEqual(image_processor.do_pad						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

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

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

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

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processing(lowerCamelCase						,   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 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase						,   numpify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   np.ndarray	)

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

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

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

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

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

     def 	UpperCamelCase(					self	):
       # Initialize image_processing
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowerCamelCase						,   torchify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   torch.Tensor	)

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

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

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

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

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

     @slow
     def 	UpperCamelCase(					self	):
       # prepare image and target
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"image_id": 39_769, "annotations": target}

       # encode them
       _snake_case     		=				DetaImageProcessor()
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([75, 75, 63, 65, 17, 17]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)






     @slow
     def 	UpperCamelCase(					self	):
       # prepare image, target and masks_path
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

       # encode them
       _snake_case     		=				DetaImageProcessor(format="coco_panoptic"	)
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   masks_path=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([17, 17, 63, 75, 75, 93]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify masks
       _snake_case     		=				822_873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()						,   lowerCamelCase	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)


