
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 logging


snake_case :		int							      =  logging.get_logger(__name__)
snake_case :		List[str]							      =  {"vocab_file": "spiece.model"}

snake_case :		List[str]							      =  {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

snake_case :		Tuple							      =  {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

snake_case :		List[str]							      =  "▁"





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(				self						,   _a						,   _a=True						,   _a=True						,   _a=False						,   _a="[CLS]"						,   _a="[SEP]"						,   _a="<unk>"						,   _a="[SEP]"						,   _a="<pad>"						,   _a="[CLS]"						,   _a="[MASK]"						,   _a = None						,   **_a						,   ):
				# Mask token behave like a normal word, i.e. include the space before it and
				# is included in the raw text, there should be a match in a non-normalized sentence.
				__magic_name__		:		str	   =     (
				    AddedToken(_a						,   lstrip=_a						,   rstrip=_a						,   normalized=_a		)
				    if isinstance(_a						,   _a		)
				    else mask_token
				)

				__magic_name__		:		Union[str, Any]	   =     {} 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						,   sp_model_kwargs=self.sp_model_kwargs						,   **_a						,   )

				__magic_name__		:		Dict	   =     do_lower_case
				__magic_name__		:		Tuple	   =     remove_space
				__magic_name__		:		Union[str, Any]	   =     keep_accents
				__magic_name__		:		Tuple	   =     vocab_file

				__magic_name__		:		int	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(_a		)







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     {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		):
				__magic_name__		:		List[str]	   =     self.__dict__.copy()
				__magic_name__		:		Any	   =     None
				return state







			def __setstate__(				self						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     d

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

				__magic_name__		:		Optional[int]	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.vocab_file		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if self.remove_space:
					__magic_name__		:		List[Any]	   =     " ".join(inputs.strip().split()		)
				else:
					__magic_name__		:		str	   =     inputs
				__magic_name__		:		int	   =     outputs.replace("``"						,   "\""		).replace("''"						,   "\""		)

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

				return outputs







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     self.preprocess_text(_a		)
				__magic_name__		:		Dict	   =     self.sp_model.encode(_a						,   out_type=_a		)
				__magic_name__		:		Any	   =     []
				for piece in pieces:
					if len(_a		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
						__magic_name__		:		Optional[Any]	   =     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:
								__magic_name__		:		List[str]	   =     cur_pieces[1:]
							else:
								__magic_name__		:		Optional[int]	   =     cur_pieces[0][1:]
						cur_pieces.append(piece[-1]		)
						new_pieces.extend(_a		)
					else:
						new_pieces.append(_a		)

				return new_pieces







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.PieceToId(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.IdToPiece(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Any	   =     []
				__magic_name__		:		Union[str, Any]	   =     ""
				__magic_name__		:		int	   =     False
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						if not prev_is_special:
							out_string += " "
						out_string += self.sp_model.decode(_a		) + token
						__magic_name__		:		List[Any]	   =     True
						__magic_name__		:		Optional[int]	   =     []
					else:
						current_sub_tokens.append(_a		)
						__magic_name__		:		Optional[Any]	   =     False
				out_string += self.sp_model.decode(_a		)
				return out_string.strip()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		List[str]	   =     [self.sep_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.cls_token_id]
				if token_ids_a is None:
					return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = 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 [1] + ([0] * len(_a		)) + [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[int]	   =     [self.sep_token_id]
				__magic_name__		:		Union[str, Any]	   =     [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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		List[str]	   =     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:
						__magic_name__		:		Union[str, Any]	   =     self.sp_model.serialized_model_proto()
						fi.write(_a		)

				return (out_vocab_file,)


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class       _snake_case					:







							@staticmethod
							def 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     hashlib.mda(image.tobytes()   )
	return m.hexdigest()[:10]






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     np.array(_snake_case   )
	__magic_name__		:		Optional[int]	   =     npimg.shape
	return {"hash": hashimage(_snake_case   ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
			UpperCamelCase__          =    dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     MaskGenerationPipeline(model=_a						,   image_processor=_a		)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				pass







			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     pipeline("mask-generation"						,   model="facebook/sam-vit-huge"		)

				__magic_name__		:		str	   =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Dict	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
				        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
				        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
				        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
				        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
				        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
				        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
				        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
				        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
				        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
				        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
				        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
				        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
				        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
				        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
				        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
				        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
				        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
				        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
				        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
				        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
				        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
				        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
				    ]						,   )
				# fmt: on







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "facebook/sam-vit-huge"
				__magic_name__		:		str	   =     pipeline("mask-generation"						,   model=_a		)

				__magic_name__		:		Tuple	   =     image_segmenter(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"						,   pred_iou_thresh=1						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Any	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				    ]						,   )
