




"""simple docstring"""







from ...processing_utils import ProcessorMixin






class 					SCREAMING_SNAKE_CASE__    (    lowercase_    ):
						_a			  =  'SpeechT5FeatureExtractor'
						_a			  =  'SpeechT5Tokenizer'


						def __init__(							self :	Dict  ,  lowerCAmelCase :	Union[str, Any]  ,  lowerCAmelCase :	str    ):
										super().__init__(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )


						def __call__(							self :	Dict  ,  *lowerCAmelCase :	Optional[Any]  ,  **lowerCAmelCase :	Union[str, Any]    ):
										lowerCAmelCase              = kwargs.pop("""audio"""  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = kwargs.pop("""text"""  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = kwargs.pop("""text_target"""  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = kwargs.pop("""audio_target"""  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = kwargs.pop("""sampling_rate"""  ,  SCREAMING_SNAKE_CASE_    )

										if audio is not None and text is not None:
														raise ValueError(
														    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""    )
										if audio_target is not None and text_target is not None:
														raise ValueError(
														    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""    )
										if audio is None and audio_target is None and text is None and text_target is None:
														raise ValueError(
														    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""    )

										if audio is not None:
														lowerCAmelCase              = self.feature_extractor(SCREAMING_SNAKE_CASE_  ,  *SCREAMING_SNAKE_CASE_  ,  sampling_rate=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
										elif text is not None:
														lowerCAmelCase              = self.tokenizer(SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
										else:
														lowerCAmelCase              = None

										if audio_target is not None:
														lowerCAmelCase              = self.feature_extractor(audio_target=SCREAMING_SNAKE_CASE_  ,  *SCREAMING_SNAKE_CASE_  ,  sampling_rate=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
														lowerCAmelCase              = targets['input_values']
										elif text_target is not None:
														lowerCAmelCase              = self.tokenizer(SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
														lowerCAmelCase              = targets['input_ids']
										else:
														lowerCAmelCase              = None

										if inputs is None:
														return targets

										if targets is not None:
														lowerCAmelCase              = labels

														lowerCAmelCase              = targets.get("""attention_mask"""    )
														if decoder_attention_mask is not None:
																		lowerCAmelCase              = decoder_attention_mask

										return inputs


						def      __lowercase       (							self :	Tuple  ,  *lowerCAmelCase :	Any  ,  **lowerCAmelCase :	Optional[int]    ):
										lowerCAmelCase              = kwargs.pop("""input_values"""  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = kwargs.pop("""input_ids"""  ,  SCREAMING_SNAKE_CASE_    )
										lowerCAmelCase              = kwargs.pop("""labels"""  ,  SCREAMING_SNAKE_CASE_    )

										if input_values is not None and input_ids is not None:
														raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""    )
										if input_values is None and input_ids is None and labels is None:
														raise ValueError(
														    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""    )

										if input_values is not None:
														lowerCAmelCase              = self.feature_extractor.pad(SCREAMING_SNAKE_CASE_  ,  *SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
										elif input_ids is not None:
														lowerCAmelCase              = self.tokenizer.pad(SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
										else:
														lowerCAmelCase              = None

										if labels is not None:
														if "input_ids" in labels or (isinstance(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ) and "input_ids" in labels[0]):
																		lowerCAmelCase              = self.tokenizer.pad(SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
																		lowerCAmelCase              = targets['input_ids']
														else:
																		lowerCAmelCase              = self.feature_extractor.feature_size
																		lowerCAmelCase              = self.feature_extractor.num_mel_bins
																		lowerCAmelCase              = self.feature_extractor.pad(SCREAMING_SNAKE_CASE_  ,  *SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
																		lowerCAmelCase              = feature_size_hack
																		lowerCAmelCase              = targets['input_values']
										else:
														lowerCAmelCase              = None

										if inputs is None:
														return targets

										if targets is not None:
														lowerCAmelCase              = labels

														lowerCAmelCase              = targets.get("""attention_mask"""    )
														if decoder_attention_mask is not None:
																		lowerCAmelCase              = decoder_attention_mask

										return inputs


						def      __lowercase       (							self :	List[Any]  ,  *lowerCAmelCase :	str  ,  **lowerCAmelCase :	Optional[Any]    ):
										return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )




						def      __lowercase       (							self :	Optional[Any]  ,  *lowerCAmelCase :	Any  ,  **lowerCAmelCase :	List[str]    ):
										return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_  ,  **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       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       )       )





