
# 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__							(     snake_case__	):
				lowercase			    =  (
				    '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			    =  'CIDAS/clipseg-rd64-refined'
				lowercase			    =  'image_segmenter'
				lowercase			    =  CLIPSegForImageSegmentation

				lowercase			    =  ['image', 'text']
				lowercase			    =  ['image']






				def __init__(						self					:      Optional[int]    ,     *a					:      Any    ,     **a					:      Optional[int]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Any    ,     a					:      List[str]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[Any]   ):






							'''simple docstring'''







							with torch.no_grad():
										lowerCAmelCase__      :      Tuple         =		self.model(**a   ).logits
							return logits






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		outputs.cpu().detach().numpy()
							lowerCAmelCase__      :      Optional[Any]         =		0
							lowerCAmelCase__      :      Tuple         =		1
							return Image.fromarray((array * 255).astype(np.uinta   )   )







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if images is None and text is None:
													raise ValueError('You have to specify either images or text.'    )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
													return text_encoding

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

							if text_encoding is not None:
													encoding_image_processor.update(UpperCAmelCase    )

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.tokenizer.model_input_names
							_UpperCAmelCase									=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
