




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :      Dict						=      get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class 	_UpperCamelCase					( A__		,				unittest.TestCase						):
      UpperCAmelCase_						=						AlbertTokenizer
      UpperCAmelCase_						=						AlbertTokenizerFast
      UpperCAmelCase_						=						True
      UpperCAmelCase_						=						True
      UpperCAmelCase_						=						True
      def 		UpperCAmelCase_  (	self      :str					)   ->	Any:
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCAmelCase__							       =  AlbertTokenizer(_a					)
       tokenizer.save_pretrained(self.tmpdirname					)
      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :Dict					)   ->	Tuple:
       UpperCAmelCase__							       =  "this is a test"
       UpperCAmelCase__							       =  "this is a test"
       return input_text, output_text
      def 		UpperCAmelCase_  (	self      :int					)   ->	Optional[int]:
       UpperCAmelCase__							       =  "<pad>"
       UpperCAmelCase__							       =  0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a					)       ,					_a					)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a					)       ,					_a					)
      def 		UpperCAmelCase_  (	self      :str					)   ->	int:
       UpperCAmelCase__							       =  list(self.get_tokenizer().get_vocab().keys()					)

       self.assertEqual(vocab_keys[0]       ,					"<pad>"					)
       self.assertEqual(vocab_keys[1]       ,					"<unk>"					)
       self.assertEqual(vocab_keys[-1]       ,					"▁eloquent"					)
       self.assertEqual(len(_a					)       ,					3_0000					)
      def 		UpperCAmelCase_  (	self      :Tuple					)   ->	List[Any]:
       self.assertEqual(self.get_tokenizer().vocab_size       ,					3_0000					)
      def 		UpperCAmelCase_  (	self      :Optional[Any]					)   ->	List[str]:






       if not self.test_rust_tokenizer:
        return

       UpperCAmelCase__							       =  self.get_tokenizer()
       UpperCAmelCase__							       =  self.get_rust_tokenizer()

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

       UpperCAmelCase__							       =  tokenizer.tokenize(_a					)
       UpperCAmelCase__							       =  rust_tokenizer.tokenize(_a					)
       self.assertListEqual(_a       ,					_a					)

       UpperCAmelCase__							       =  tokenizer.encode(_a       ,					add_special_tokens=_a					)
       UpperCAmelCase__							       =  rust_tokenizer.encode(_a       ,					add_special_tokens=_a					)
       self.assertListEqual(_a       ,					_a					)

       UpperCAmelCase__							       =  self.get_rust_tokenizer()
       UpperCAmelCase__							       =  tokenizer.encode(_a					)
       UpperCAmelCase__							       =  rust_tokenizer.encode(_a					)
       self.assertListEqual(_a       ,					_a					)
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	Tuple:
       UpperCAmelCase__							       =  AlbertTokenizer(_a       ,					keep_accents=_a					)

       UpperCAmelCase__							       =  tokenizer.tokenize("This is a test"					)
       self.assertListEqual(_a       ,					["▁this", "▁is", "▁a", "▁test"]					)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(_a					)       ,					[48, 25, 21, 1289]					)

       UpperCAmelCase__							       =  tokenizer.tokenize("I was born in 92000, and this is falsé."					)
       self.assertListEqual(
           _a       ,					["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]					)
       UpperCAmelCase__							       =  tokenizer.convert_tokens_to_ids(_a					)
       self.assertListEqual(_a       ,					[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]					)

       UpperCAmelCase__							       =  tokenizer.convert_ids_to_tokens(_a					)
       self.assertListEqual(
           _a       ,					["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]       ,					)
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	Dict:
       UpperCAmelCase__							       =  AlbertTokenizer(_a					)

       UpperCAmelCase__							       =  tokenizer.encode("sequence builders"					)
       UpperCAmelCase__							       =  tokenizer.encode("multi-sequence build"					)

       UpperCAmelCase__							       =  tokenizer.build_inputs_with_special_tokens(_a					)
       UpperCAmelCase__							       =  tokenizer.build_inputs_with_special_tokens(_a       ,					_a					)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]







      @slow
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	int:
       UpperCAmelCase__							       =  {"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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_a       ,					model_name="albert-base-v2"       ,					revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"       ,					)








'''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__    (       A__  ):
  A__		 =				(
      '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.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
