"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class 				snake_case__     (   unittest.TestCase   ):


	def 		a__ (				self		):
								__a     				=  tempfile.mkdtemp()

								# fmt: off
								__a     				=  ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
								# fmt: on
								__a     				=  dict(zip(_a  ,						range(len(_a		)		)		)		)
								__a     				=  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
								__a     				=  {"unk_token": "<unk>"}

								__a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["vocab_file"]		)
								__a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["merges_file"]		)
								with open(self.vocab_file  ,						"w"  ,						encoding="utf-8"		) as fp:
															fp.write(json.dumps(_a		) + "\n"		)
								with open(self.merges_file  ,						"w"  ,						encoding="utf-8"		) as fp:
															fp.write("\n".join(_a		)		)

								__a     				=  {
								    "do_resize": True,
								    "size": 20,
								    "do_center_crop": True,
								    "crop_size": 18,
								    "do_normalize": True,
								    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
								    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
								}
								__a     				=  os.path.join(self.tmpdirname  ,						_a		)
								with open(self.image_processor_file  ,						"w"  ,						encoding="utf-8"		) as fp:
															json.dump(_a  ,						_a		)


	def 		a__ (				self  ,						**lowerCamelCase		):
								return CLIPTokenizer.from_pretrained(self.tmpdirname  ,						**_a		)


	def 		a__ (				self  ,						**lowerCamelCase		):
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,						**_a		)


	def 		a__ (				self  ,						**lowerCamelCase		):
								return ViTImageProcessor.from_pretrained(self.tmpdirname  ,						**_a		)


	def 		a__ (				self		):
								shutil.rmtree(self.tmpdirname		)


	def 		a__ (				self		):
								__a     				=  [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta		)]

								__a     				=  [Image.fromarray(np.moveaxis(_a  ,						0  ,						-1		)		) for x in image_inputs]

								return image_inputs


	def 		a__ (				self		):
								__a     				=  self.get_tokenizer()
								__a     				=  self.get_rust_tokenizer()
								__a     				=  self.get_image_processor()

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)
								processor_slow.save_pretrained(self.tmpdirname		)
								__a     				=  CLIPSegProcessor.from_pretrained(self.tmpdirname  ,						use_fast=_a		)

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)
								processor_fast.save_pretrained(self.tmpdirname		)
								__a     				=  CLIPSegProcessor.from_pretrained(self.tmpdirname		)

								self.assertEqual(processor_slow.tokenizer.get_vocab()  ,						tokenizer_slow.get_vocab()		)
								self.assertEqual(processor_fast.tokenizer.get_vocab()  ,						tokenizer_fast.get_vocab()		)
								self.assertEqual(tokenizer_slow.get_vocab()  ,						tokenizer_fast.get_vocab()		)
								self.assertIsInstance(processor_slow.tokenizer  ,						_a		)
								self.assertIsInstance(processor_fast.tokenizer  ,						_a		)

								self.assertEqual(processor_slow.image_processor.to_json_string()  ,						image_processor.to_json_string()		)
								self.assertEqual(processor_fast.image_processor.to_json_string()  ,						image_processor.to_json_string()		)
								self.assertIsInstance(processor_slow.image_processor  ,						_a		)
								self.assertIsInstance(processor_fast.image_processor  ,						_a		)


	def 		a__ (				self		):
								__a     				=  CLIPSegProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()		)
								processor.save_pretrained(self.tmpdirname		)

								__a     				=  self.get_tokenizer(bos_token="(BOS)"  ,						eos_token="(EOS)"		)
								__a     				=  self.get_image_processor(do_normalize=_a  ,						padding_value=1.0		)

								__a     				=  CLIPSegProcessor.from_pretrained(
								    self.tmpdirname  ,						bos_token="(BOS)"  ,						eos_token="(EOS)"  ,						do_normalize=_a  ,						padding_value=1.0		)

								self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer_add_kwargs.get_vocab()		)
								self.assertIsInstance(processor.tokenizer  ,						_a		)

								self.assertEqual(processor.image_processor.to_json_string()  ,						image_processor_add_kwargs.to_json_string()		)
								self.assertIsInstance(processor.image_processor  ,						_a		)


	def 		a__ (				self		):
								__a     				=  self.get_image_processor()
								__a     				=  self.get_tokenizer()

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)

								__a     				=  self.prepare_image_inputs()

								__a     				=  image_processor(_a  ,						return_tensors="np"		)
								__a     				=  processor(images=_a  ,						return_tensors="np"		)

								for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2		)


	def 		a__ (				self		):
								__a     				=  self.get_image_processor()
								__a     				=  self.get_tokenizer()

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)

								__a     				=  "lower newer"

								__a     				=  processor(text=_a		)

								__a     				=  tokenizer(_a		)

								for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]		)


	def 		a__ (				self		):
								__a     				=  self.get_image_processor()
								__a     				=  self.get_tokenizer()

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)

								__a     				=  "lower newer"
								__a     				=  self.prepare_image_inputs()

								__a     				=  processor(text=_a  ,						images=_a		)

								self.assertListEqual(list(inputs.keys()		)  ,						["input_ids", "attention_mask", "pixel_values"]		)

								# test if it raises when no input is passed
								with pytest.raises(_a		):
															processor()


	def 		a__ (				self		):
								__a     				=  self.get_image_processor()
								__a     				=  self.get_tokenizer()

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)

								__a     				=  self.prepare_image_inputs()
								__a     				=  self.prepare_image_inputs()

								__a     				=  processor(images=_a  ,						visual_prompt=_a		)

								self.assertListEqual(list(inputs.keys()		)  ,						["pixel_values", "conditional_pixel_values"]		)

								# test if it raises when no input is passed
								with pytest.raises(_a		):
															processor()






	def 		a__ (				self		):
								__a     				=  self.get_image_processor()
								__a     				=  self.get_tokenizer()

								__a     				=  CLIPSegProcessor(tokenizer=_a  ,						image_processor=_a		)

								__a     				=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__a     				=  processor.batch_decode(_a		)
								__a     				=  tokenizer.batch_decode(_a		)

								self.assertListEqual(_a  ,						_a		)

"""simple docstring"""



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_camembert import CamembertTokenizer
else:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


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

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						additional_special_tokens=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [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 + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
