from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__											=					{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__											=					['MobileBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__											=					[
          'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MobileBertForMaskedLM',
          'MobileBertForMultipleChoice',
          'MobileBertForNextSentencePrediction',
          'MobileBertForPreTraining',
          'MobileBertForQuestionAnswering',
          'MobileBertForSequenceClassification',
          'MobileBertForTokenClassification',
          'MobileBertLayer',
          'MobileBertModel',
          'MobileBertPreTrainedModel',
          'load_tf_weights_in_mobilebert',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase__											=					[
          'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFMobileBertForMaskedLM',
          'TFMobileBertForMultipleChoice',
          'TFMobileBertForNextSentencePrediction',
          'TFMobileBertForPreTraining',
          'TFMobileBertForQuestionAnswering',
          'TFMobileBertForSequenceClassification',
          'TFMobileBertForTokenClassification',
          'TFMobileBertMainLayer',
          'TFMobileBertModel',
          'TFMobileBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mobilebert import (
          MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileBertConfig,
          MobileBertOnnxConfig,
      )
      from .tokenization_mobilebert import MobileBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mobilebert_fast import MobileBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilebert import (
                MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileBertForMaskedLM,
                MobileBertForMultipleChoice,
                MobileBertForNextSentencePrediction,
                MobileBertForPreTraining,
                MobileBertForQuestionAnswering,
                MobileBertForSequenceClassification,
                MobileBertForTokenClassification,
                MobileBertLayer,
                MobileBertModel,
                MobileBertPreTrainedModel,
                load_tf_weights_in_mobilebert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilebert import (
                TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileBertForMaskedLM,
                TFMobileBertForMultipleChoice,
                TFMobileBertForNextSentencePrediction,
                TFMobileBertForPreTraining,
                TFMobileBertForQuestionAnswering,
                TFMobileBertForSequenceClassification,
                TFMobileBertForTokenClassification,
                TFMobileBertMainLayer,
                TFMobileBertModel,
                TFMobileBertPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase__											=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""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__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (
          """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."""
      )
      _SCREAMING_SNAKE_CASE  				=     """CIDAS/clipseg-rd64-refined"""
      _SCREAMING_SNAKE_CASE  				=     """image_segmenter"""
      _SCREAMING_SNAKE_CASE  				=     CLIPSegForImageSegmentation

      _SCREAMING_SNAKE_CASE  				=     ["""image""", """text"""]
      _SCREAMING_SNAKE_CASE  				=     ["""image"""]



      def __init__(      self				:      Dict    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        requires_backends(self    ,			['vision']       )
        super().__init__(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      "Image"    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        return self.pre_processor(text=[label]    ,			images=[image]    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        with torch.no_grad():
          lowerCAmelCase_   :				List[str]								= self.model(**SCREAMING_SNAKE_CASE_       ).logits
        return logits





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= outputs.cpu().detach().numpy()
        lowerCAmelCase_   :				Optional[Any]								= 0
        lowerCAmelCase_   :				Optional[Any]								= 1
        return Image.fromarray((array * 2_5_5).astype(np.uinta       )       )





