





'''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 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Union[str, Any]			=  (
          '''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.'''
      )
      _lowercase:     str			=  '''CIDAS/clipseg-rd64-refined'''
      _lowercase:     Union[str, Any]			=  '''image_segmenter'''
      _lowercase:     Optional[Any]			=  CLIPSegForImageSegmentation

      _lowercase:     Dict			=  ['''image''', '''text''']
      _lowercase:     List[str]			=  ['''image''']




      def __init__(     self     :					Dict     ,     *__snake_case     :					Optional[Any]     ,     **__snake_case     :					Optional[Any]    )							-> List[Any]:
            requires_backends(self     ,     ["""vision"""]    )
            super().__init__(*__snake_case     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					"Image"     ,     __snake_case     :					str    )							-> str:
            return self.pre_processor(text=[label]     ,     images=[image]     ,     padding=__snake_case     ,     return_tensors="""pt"""    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Dict    )							-> Union[str, Any]:
            with torch.no_grad():
                  _lowerCAmelCase					=				self.model(**__snake_case    ).logits
            return logits




      def 							lowercase__       (     self     :					int     ,     __snake_case     :					List[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				outputs.cpu().detach().numpy()
            _lowerCAmelCase					=				0
            _lowerCAmelCase					=				1
            return Image.fromarray((array * 2_55).astype(np.uinta    )    )













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






