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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							:Optional[int]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[Any]										=    "▁"

SCREAMING_SNAKE_CASE							:int										=    {"vocab_file": "sentencepiece.bpe.model"}

SCREAMING_SNAKE_CASE							:List[str]										=    {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

SCREAMING_SNAKE_CASE							:Optional[int]										=    {
    "facebook/xglm-564M": 2048,
}





class 							UpperCAmelCase				(  _SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					["input_ids", "attention_mask"]






	def __init__(	self :			List[str]							,A :			Dict							,A :			str="<s>"							,A :			Any="</s>"							,A :			List[str]="</s>"							,A :			str="<s>"							,A :			Optional[int]="<unk>"							,A :			str="<pad>"							,A :			Dict = None							,**A :			Dict							,):
					__A             =			{} if sp_model_kwargs is None else sp_model_kwargs

					# Compatibility with the original tokenizer
					__A             =			7
					__A             =			[f'''<madeupword{i}>''' for i in range(self.num_madeup_words			)]

					__A             =			kwargs.get("additional_special_tokens"							,[]			)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A             =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(str(A			)			)
					__A             =			vocab_file

					# Original fairseq vocab and spm vocab must be "aligned":
					# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
					# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
					# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
					# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

					# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
					__A             =			1

					# Mimic fairseq token-to-id alignment for the first 4 token
					__A             =			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					__A             =			len(self.sp_model			)
					__A             =			{f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
					self.fairseq_tokens_to_ids.update(A			)

					__A             =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def __getstate__(	self :			Union[str, Any]			):
					__A             =			self.__dict__.copy()
					__A             =			None
					__A             =			self.sp_model.serialized_model_proto()
					return state






	def __setstate__(	self :			int							,A :			List[str]			):
					__A             =			d

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

					__A             =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[str]							,A :			List[str] = None			):

					if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
					__A             =			[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a






	def     UpperCamelCase_  (	self :			Tuple							,A :			Optional[int]							,A :			List[str] = None							,A :			List[str] = 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 None:
									return [1] + ([0] * len(A			))
					return [1] + ([0] * len(A			)) + [1, 1] + ([0] * len(A			))






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			str = None			):
					__A             =			[self.sep_token_id]

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






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words






	def     UpperCamelCase_  (	self :			str			):
					__A             =			{self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
					vocab.update(self.added_tokens_encoder			)
					return vocab






	def     UpperCamelCase_  (	self :			List[str]							,A :			str			):
					return self.sp_model.encode(A							,out_type=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Any			):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					__A             =			self.sp_model.PieceToId(A			)

					# Need to return unknown token if the SP model returned 0
					return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






	def     UpperCamelCase_  (	self :			Dict							,A :			int			):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset			)






	def     UpperCamelCase_  (	self :			int							,A :			str			):
					__A             =			"".join(A			).replace(A							," "			).strip()
					return out_string







	def     UpperCamelCase_  (	self :			Dict							,A :			str							,A :			int = None			):
					if not os.path.isdir(A			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					__A             =			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:
													__A             =			self.sp_model.serialized_model_proto()
													fi.write(A			)

					return (out_vocab_file,)


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 PoolFormerImageProcessor





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Optional[int]							,A :			List[str]							,A :			List[Any]=7							,A :			Any=3							,A :			int=30							,A :			List[Any]=4_00							,A :			str=True							,A :			int=None							,A :			List[str]=0.9							,A :			Dict=None							,A :			int=True							,A :			Any=[0.5, 0.5, 0.5]							,A :			Optional[int]=[0.5, 0.5, 0.5]							,):
					__A             =			size if size is not None else {"shortest_edge": 30}
					__A             =			crop_size if crop_size is not None else {"height": 30, "width": 30}
					__A             =			parent
					__A             =			batch_size
					__A             =			num_channels
					__A             =			min_resolution
					__A             =			max_resolution
					__A             =			do_resize_and_center_crop
					__A             =			size
					__A             =			crop_pct
					__A             =			crop_size
					__A             =			do_normalize
					__A             =			image_mean
					__A             =			image_std







	def     UpperCamelCase_  (	self :			int			):
					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}





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




	'''simple docstring'''





	snake_case_    =					PoolFormerImageProcessor if is_vision_available() else None






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			PoolFormerImageProcessingTester(self			)






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self.image_processor_tester.prepare_image_processor_dict()






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(A							,"do_resize_and_center_crop"			)			)
					self.assertTrue(hasattr(A							,"size"			)			)
					self.assertTrue(hasattr(A							,"crop_pct"			)			)
					self.assertTrue(hasattr(A							,"do_normalize"			)			)
					self.assertTrue(hasattr(A							,"image_mean"			)			)
					self.assertTrue(hasattr(A							,"image_std"			)			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size							,{"shortest_edge": 30}			)
					self.assertEqual(image_processor.crop_size							,{"height": 30, "width": 30}			)

					__A             =			self.image_processing_class.from_dict(self.image_processor_dict							,size=42							,crop_size=84			)
					self.assertEqual(image_processor.size							,{"shortest_edge": 42}			)
					self.assertEqual(image_processor.crop_size							,{"height": 84, "width": 84}			)






	def     UpperCamelCase_  (	self :			List[str]			):
					pass






	def     UpperCamelCase_  (	self :			Optional[int]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__A             =			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             =			image_processing(image_inputs[0]							,return_tensors="pt"			).pixel_values

					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)

					# Test batched
					__A             =			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,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)






	def     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__A             =			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             =			image_processing(image_inputs[0]							,return_tensors="pt"			).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)

					# Test batched
					__A             =			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,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)







	def     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__A             =			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             =			image_processing(image_inputs[0]							,return_tensors="pt"			).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)

					# Test batched
					__A             =			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,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )							,)
