




"""simple docstring"""







import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

a            =							logging.getLogger()





def 							lowercase    (snake_case__      :       str       )   ->     Dict:


				'''simple docstring'''






				lowerCAmelCase              = {}
				lowerCAmelCase              = os.path.join(lowerCAmelCase__					,				"""all_results.json"""       )
				if os.path.exists(lowerCAmelCase__       ):
								with open(lowerCAmelCase__					,				"""r"""       ) as f:
												lowerCAmelCase              = json.load(lowerCAmelCase__       )
				else:
								raise ValueError(f'''can\'t find {path}'''       )
				return results


a            =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






@require_torch_tpu
class 					SCREAMING_SNAKE_CASE__    (    lowercase_    ):


						def      __lowercase       (							self :	Any    ):
										import xla_spawn

										lowerCAmelCase              = self.get_auto_remove_tmp_dir()
										lowerCAmelCase              = f'''\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '''.split()

										with patch.object(SCREAMING_SNAKE_CASE_  ,  """argv"""  ,  SCREAMING_SNAKE_CASE_    ):
														lowerCAmelCase              = time()
														xla_spawn.main()
														lowerCAmelCase              = time()

														lowerCAmelCase              = get_results(SCREAMING_SNAKE_CASE_    )
														self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.75    )

														# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
														self.assertLess(end - start  ,  500    )




						def      __lowercase       (							self :	List[str]    ):
										import xla_spawn

										lowerCAmelCase              = '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
										with patch.object(SCREAMING_SNAKE_CASE_  ,  """argv"""  ,  SCREAMING_SNAKE_CASE_    ):
														xla_spawn.main()










"""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       )       )





