import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				tempfile.mkdtemp()

        A_							:       str	      =				CLIPImageProcessor()
        A_							:       List[Any]	      =				BertTokenizer.from_pretrained(
            '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']      )

        A_							:       Optional[int]	      =				GitProcessor(__magic_name__ ,       __magic_name__      )

        processor.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).tokenizer
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).image_processor
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        shutil.rmtree(self.tmpdirname      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				[np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta      )]

        A_							:       Union[str, Any]	      =				[Image.fromarray(np.moveaxis(__magic_name__ ,       0 ,       -1      )      ) for x in image_inputs]

        return image_inputs
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        A_							:       int	      =				self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''      )
        A_							:       str	      =				self.get_image_processor(do_normalize=__magic_name__ ,       padding_value=1.0      )

        A_							:       int	      =				GitProcessor.from_pretrained(
            self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=__magic_name__ ,       padding_value=1.0      )

        self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()      )
        self.assertIsInstance(processor.tokenizer ,       __magic_name__      )

        self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()      )
        self.assertIsInstance(processor.image_processor ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       str	      =				self.prepare_image_inputs()

        A_							:       List[str]	      =				image_processor(__magic_name__ ,       return_tensors='''np'''      )
        A_							:       int	      =				processor(images=__magic_name__ ,       return_tensors='''np'''      )

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1e-2      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       str	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''

        A_							:       Tuple	      =				processor(text=__magic_name__      )

        A_							:       List[Any]	      =				tokenizer(__magic_name__ ,       return_token_type_ids=__magic_name__      )

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				'''lower newer'''
        A_							:       Dict	      =				self.prepare_image_inputs()

        A_							:       Union[str, Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )

        # test if it raises when no input is passed
        with pytest.raises(__magic_name__      ):
               processor()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A_							:       List[Any]	      =				processor.batch_decode(__magic_name__      )
        A_							:       Dict	      =				tokenizer.batch_decode(__magic_name__      )

        self.assertListEqual(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       str	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''
        A_							:       List[str]	      =				self.prepare_image_inputs()

        A_							:       Optional[Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )



from typing import Any, Dict, 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():
    from ..models.auto.modeling_auto import (
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


_lowerCAmelCase        =       Dict[str, Any]
_lowerCAmelCase        =       List[Prediction]






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""      )

        requires_backends(self ,       '''vision'''      )
        self.check_model_type(
            dict(
                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				{}
        A_							:       List[Any]	      =				{}
        if "subtask" in kwargs:
               A_							:       Optional[int]	      =				kwargs['''subtask''']
               A_							:       Dict	      =				kwargs['''subtask''']
        if "threshold" in kwargs:
               A_							:       Tuple	      =				kwargs['''threshold''']
        if "mask_threshold" in kwargs:
               A_							:       int	      =				kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
               A_							:       Optional[Any]	      =				kwargs['''overlap_mask_area_threshold''']

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				load_image(__magic_name__      )
        A_							:       int	      =				[(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
               if subtask is None:
                      A_							:       int	      =				{}
               else:
                      A_							:       str	      =				{'''task_inputs''': [subtask]}
               A_							:       List[Any]	      =				self.image_processor(images=[image] ,       return_tensors='''pt''' ,       **__magic_name__      )
               A_							:       int	      =				self.tokenizer(
                   inputs['''task_inputs'''] ,       padding='''max_length''' ,       max_length=self.model.config.task_seq_len ,       return_tensors=self.framework ,       )['''input_ids''']
        else:
               A_							:       Any	      =				self.image_processor(images=[image] ,       return_tensors='''pt'''      )
        A_							:       Optional[int]	      =				target_size
        return inputs
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        if subtask in {"panoptic", None} and hasattr(self.image_processor ,       '''post_process_panoptic_segmentation'''      ):
               A_							:       Dict	      =				self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor ,       '''post_process_instance_segmentation'''      ):
               A_							:       Tuple	      =				self.image_processor.post_process_instance_segmentation

        if fn is not None:
               A_							:       Dict	      =				fn(
                   __magic_name__ ,       threshold=__magic_name__ ,       mask_threshold=__magic_name__ ,       overlap_mask_area_threshold=__magic_name__ ,       target_sizes=model_outputs['''target_size'''] ,       )[0]

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

               for segment in outputs["segments_info"]:
                      A_							:       Optional[int]	      =				(segmentation == segment['''id''']) * 255
                      A_							:       int	      =				Image.fromarray(mask.numpy().astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       Union[str, Any]	      =				self.model.config.idalabel[segment['''label_id''']]
                      A_							:       Optional[int]	      =				segment['''score''']
                      annotation.append({'''score''': score, '''label''': label, '''mask''': mask}      )

        elif subtask in {"semantic", None} and hasattr(self.image_processor ,       '''post_process_semantic_segmentation'''      ):
               A_							:       int	      =				self.image_processor.post_process_semantic_segmentation(
                   __magic_name__ ,       target_sizes=model_outputs['''target_size''']      )[0]

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				self.model.config.idalabel[label]
                      annotation.append({'''score''': None, '''label''': label, '''mask''': mask}      )
        else:
               raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model      )}"""      )
        return annotation


