



from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_lowercase												=		logging.get_logger(__name__)



@add_end_docstrings(A__     )
class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						*a__ ,						**a__):
         super().__init__(*a__ ,						**a__)
         requires_backends(self ,						'''vision''')
         self.check_model_type(a__)




     def __call__(				self ,						a__ ,						**a__):
         return super().__call__(a__ ,						**a__)




     def 		snake_case_		(				self ,						**a__):
         return {}, {}, {}




     def 		snake_case_		(				self ,						a__):
         A__		 =					load_image(a__)
         A__		 =					image.size
         A__		 =					self.image_processor(images=a__ ,						return_tensors=self.framework)
         return model_inputs




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.model(**a__)
         return model_outputs




     def 		snake_case_		(				self ,						a__):
         A__		 =					model_outputs.predicted_depth
         A__		 =					torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1) ,						size=self.image_size[::-1] ,						mode='''bicubic''' ,						align_corners=a__)
         A__		 =					prediction.squeeze().cpu().numpy()
         A__		 =					(output * 2_5_5 / np.max(a__)).astype('''uint8''')
         A__		 =					Image.fromarray(a__)
         A__		 =					{}
         A__		 =					predicted_depth
         A__		 =					depth
         return output_dict





# 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.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''Salesforce/blip-image-captioning-base'''
     UpperCamelCase__		    =				(
         '''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
         '''image to caption, and returns a text that contains the description in English.'''
     )
     UpperCamelCase__		    =				'''image_captioner'''
     UpperCamelCase__		    =				AutoModelForVisionaSeq

     UpperCamelCase__		    =				['''image''']
     UpperCamelCase__		    =				['''text''']




     def __init__(				self ,						*a__ ,						**a__):
         requires_backends(self ,						['''vision'''])
         super().__init__(*a__ ,						**a__)




     def 		snake_case_		(				self ,						a__):
         return self.pre_processor(images=a__ ,						return_tensors='''pt''')




     def 		snake_case_		(				self ,						a__):
         return self.model.generate(**a__)




     def 		snake_case_		(				self ,						a__):
         return self.pre_processor.batch_decode(a__ ,						skip_special_tokens=a__)[0].strip()
