"""simple docstring"""




from typing import List

import numpy as np







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {key: len(lowerCamelCase__     ) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase__			,   lowerCamelCase__     )}
		if len(set(lists_lengths.values()     )     ) > 1:
				raise RuntimeError(
				    (
				        """Sharding is ambiguous for this dataset: """
				        + """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
				        + """\n""".join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()     )
				        + """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
				        + """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
				    )     )
		lowerCAmelCase__        =   max(lists_lengths.values()			,   default=0     )
		return max(1			,   lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []
		for group_idx in range(lowerCamelCase__     ):
				lowerCAmelCase__        =   num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
				if num_shards_to_add == 0:
						break
				lowerCAmelCase__        =   shards_indices_per_group[-1].stop if shards_indices_per_group else 0
				lowerCAmelCase__        =   range(lowerCamelCase__			,   start + num_shards_to_add     )
				shards_indices_per_group.append(lowerCamelCase__     )
		return shards_indices_per_group







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   _number_of_shards_in_gen_kwargs(lowerCamelCase__     )
		if num_shards == 1:
				return [dict(lowerCamelCase__     )]
		else:
				lowerCAmelCase__        =   _distribute_shards(num_shards=lowerCamelCase__			,   max_num_jobs=lowerCamelCase__     )
				return [
				    {
				        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
				        if isinstance(lowerCamelCase__			,   lowerCamelCase__     )
				        else value
				        for key, value in gen_kwargs.items()
				    }
				    for group_idx in range(len(lowerCamelCase__     )     )
				]







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return {
		    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
		    if isinstance(gen_kwargs_list[0][key]			,   lowerCamelCase__     )
		    else gen_kwargs_list[0][key]
		    for key in gen_kwargs_list[0]
		}







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {len(lowerCamelCase__     ) for value in gen_kwargs.values() if isinstance(lowerCamelCase__			,   lowerCamelCase__     )}
		lowerCAmelCase__        =   {}
		for size in list_sizes:
				lowerCAmelCase__        =   list(range(lowerCamelCase__     )     )
				rng.shuffle(indices_per_size[size]     )
		# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
		lowerCAmelCase__        =   dict(lowerCamelCase__     )
		for key, value in shuffled_kwargs.items():
				if isinstance(lowerCamelCase__			,   lowerCamelCase__     ):
						lowerCAmelCase__        =   [value[i] for i in indices_per_size[len(lowerCamelCase__     )]]
		return shuffled_kwargs





"""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       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "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."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




