"""simple docstring"""




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase    :							Optional[int]													=						get_tests_dir("fixtures/test_sentencepiece.model")
__lowerCAmelCase    :							Tuple													=						get_tests_dir("fixtures/test_sentencepiece_bpe.model")

__lowerCAmelCase    :							int													=						'pt' if is_torch_available() else 'tf'

@require_sentencepiece
@require_tokenizers
class       a_      (     lowerCAmelCase__ ,		unittest.TestCase			):
					UpperCamelCase_ :	Tuple			=    CamembertTokenizer
					UpperCamelCase_ :	Any			=    CamembertTokenizerFast
					UpperCamelCase_ :	List[str]			=    True
					UpperCamelCase_ :	Union[str, Any]			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__        =   CamembertTokenizer(_SCREAMING_SNAKE_CASE     )
							tokenizer.save_pretrained(self.tmpdirname     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   """<pad>"""
							lowerCAmelCase__        =   1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE     )  ,						_SCREAMING_SNAKE_CASE     )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE     )  ,						_SCREAMING_SNAKE_CASE     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   list(self.get_tokenizer().get_vocab().keys()     )

							self.assertEqual(vocab_keys[0]  ,						"""<s>NOTUSED"""     )
							self.assertEqual(vocab_keys[1]  ,						"""<pad>"""     )
							self.assertEqual(vocab_keys[-1]  ,						"""<mask>"""     )
							self.assertEqual(len(_SCREAMING_SNAKE_CASE     )  ,						1004     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,						1005     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   CamembertTokenizer(_SCREAMING_SNAKE_CASE     )
							tokenizer.save_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   CamembertTokenizerFast.from_pretrained(self.tmpdirname     )

							lowerCAmelCase__        =   """I was born in 92000, and this is falsé."""

							lowerCAmelCase__        =   tokenizer.encode(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase__        =   rust_tokenizer.encode(_SCREAMING_SNAKE_CASE     )
							self.assertListEqual(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase__        =   tokenizer.encode(_SCREAMING_SNAKE_CASE  ,						add_special_tokens=_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase__        =   rust_tokenizer.encode(_SCREAMING_SNAKE_CASE  ,						add_special_tokens=_SCREAMING_SNAKE_CASE     )
							self.assertListEqual(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE     )

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							lowerCAmelCase__        =   tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase__        =   rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE     )
							self.assertListEqual(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							if not self.test_rust_tokenizer:
									return

							lowerCAmelCase__        =   self.get_tokenizer()
							lowerCAmelCase__        =   self.get_rust_tokenizer()

							lowerCAmelCase__        =   """I was born in 92000, and this is falsé."""

							lowerCAmelCase__        =   tokenizer.tokenize(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase__        =   rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE     )
							self.assertListEqual(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase__        =   tokenizer.encode(_SCREAMING_SNAKE_CASE  ,						add_special_tokens=_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase__        =   rust_tokenizer.encode(_SCREAMING_SNAKE_CASE  ,						add_special_tokens=_SCREAMING_SNAKE_CASE     )
							self.assertListEqual(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE     )

							lowerCAmelCase__        =   self.get_rust_tokenizer()
							lowerCAmelCase__        =   tokenizer.encode(_SCREAMING_SNAKE_CASE     )
							lowerCAmelCase__        =   rust_tokenizer.encode(_SCREAMING_SNAKE_CASE     )
							self.assertListEqual(_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							# fmt: off
							lowerCAmelCase__        =   {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							lowerCAmelCase__        =   [
							    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
							    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
							    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
							    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
							    """telles que la traduction et la synthèse de texte.""",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_SCREAMING_SNAKE_CASE  ,						model_name="""camembert-base"""  ,						revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""  ,						sequences=_SCREAMING_SNAKE_CASE  ,						)





"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
			from PIL import Image

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
					    "It takes two arguments named `image` which should be the original image, and `label` which should be a text "
					    "describing the elements what should be identified in the segmentation mask. The tool returns the mask."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




