






'''simple docstring'''







import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import FlavaImageProcessor, FlavaProcessor
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )






@require_vision
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Union[str, Any]:
           __A		   =      tempfile.mkdtemp()

           # fmt: off
           __A		   =      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
           # fmt: on
           __A		   =      os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]					)

           with open(self.vocab_file				,				"w"				,				encoding="utf-8"					) as fp:
                 fp.write("".join([x + "\n" for x in vocab_tokens]					)					)

           __A		   =      {
               "image_mean": FLAVA_IMAGE_MEAN,
               "image_std": FLAVA_IMAGE_STD,
               "do_normalize": True,
               "do_resize": True,
               "size": 224,
               "do_center_crop": True,
               "crop_size": 224,
               "input_size_patches": 14,
               "total_mask_patches": 75,
               "mask_group_max_patches": None,
               "mask_group_min_patches": 16,
               "mask_group_min_aspect_ratio": 0.3,
               "mask_group_max_aspect_ratio": None,
               "codebook_do_resize": True,
               "codebook_size": 112,
               "codebook_do_center_crop": True,
               "codebook_crop_size": 112,
               "codebook_do_map_pixels": True,
               "codebook_do_normalize": True,
               "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
               "codebook_image_std": FLAVA_CODEBOOK_STD,
           }

           __A		   =      os.path.join(self.tmpdirname				,				lowercase__					)
           with open(self.image_processor_file				,				"w"				,				encoding="utf-8"					) as fp:
                 json.dump(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]				,				**lowercase__    :	Optional[Any]					)  ->       List[Any]:
           return BertTokenizer.from_pretrained(self.tmpdirname				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				**lowercase__    :	int					)  ->       Union[str, Any]:
           return BertTokenizerFast.from_pretrained(self.tmpdirname				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	str				,				**lowercase__    :	Any					)  ->       Dict:
           return FlavaImageProcessor.from_pretrained(self.tmpdirname				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Optional[int]:
           shutil.rmtree(self.tmpdirname					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Tuple:
           __A		   =      [np.random.randint(255				,				size=(3, 30, 400)				,				dtype=np.uinta					)]

           __A		   =      [Image.fromarray(np.moveaxis(lowercase__				,				0				,				-1					)					) for x in image_inputs]

           return image_inputs






     def     _UpperCamelCase   (self    :	int					)  ->       Optional[Any]:
           __A		   =      self.get_tokenizer()
           __A		   =      self.get_rust_tokenizer()
           __A		   =      self.get_image_processor()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)
           processor_slow.save_pretrained(self.tmpdirname					)
           __A		   =      FlavaProcessor.from_pretrained(self.tmpdirname				,				use_fast=lowercase__					)

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)
           processor_fast.save_pretrained(self.tmpdirname					)
           __A		   =      FlavaProcessor.from_pretrained(self.tmpdirname					)

           self.assertEqual(processor_slow.tokenizer.get_vocab()				,				tokenizer_slow.get_vocab()					)
           self.assertEqual(processor_fast.tokenizer.get_vocab()				,				tokenizer_fast.get_vocab()					)
           self.assertEqual(tokenizer_slow.get_vocab()				,				tokenizer_fast.get_vocab()					)
           self.assertIsInstance(processor_slow.tokenizer				,				lowercase__					)
           self.assertIsInstance(processor_fast.tokenizer				,				lowercase__					)

           self.assertEqual(processor_slow.image_processor.to_json_string()				,				image_processor.to_json_string()					)
           self.assertEqual(processor_fast.image_processor.to_json_string()				,				image_processor.to_json_string()					)
           self.assertIsInstance(processor_slow.image_processor				,				lowercase__					)
           self.assertIsInstance(processor_fast.image_processor				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[Any]:
           __A		   =      FlavaProcessor(tokenizer=self.get_tokenizer()				,				image_processor=self.get_image_processor()					)
           processor.save_pretrained(self.tmpdirname					)

           __A		   =      self.get_tokenizer(bos_token="(BOS)"				,				eos_token="(EOS)"					)
           __A		   =      self.get_image_processor(do_normalize=lowercase__				,				padding_value=1.0					)

           __A		   =      FlavaProcessor.from_pretrained(
               self.tmpdirname				,				bos_token="(BOS)"				,				eos_token="(EOS)"				,				do_normalize=lowercase__				,				padding_value=1.0					)

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

           self.assertEqual(processor.image_processor.to_json_string()				,				image_processor_add_kwargs.to_json_string()					)
           self.assertIsInstance(processor.image_processor				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Any					)  ->       Dict:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      self.prepare_image_inputs()

           __A		   =      image_processor(lowercase__				,				return_tensors="np"					)
           __A		   =      processor(images=lowercase__				,				return_tensors="np"					)

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

           # With rest of the args
           random.seed(1234					)
           __A		   =      image_processor(
               lowercase__				,				return_image_mask=lowercase__				,				return_codebook_pixels=lowercase__				,				return_tensors="np"					)
           random.seed(1234					)
           __A		   =      processor(
               images=lowercase__				,				return_image_mask=lowercase__				,				return_codebook_pixels=lowercase__				,				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    :	Union[str, Any]					)  ->       List[str]:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      "lower newer"

           __A		   =      processor(text=lowercase__					)

           __A		   =      tokenizer(lowercase__					)

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






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       int:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      "lower newer"
           __A		   =      self.prepare_image_inputs()

           __A		   =      processor(text=lowercase__				,				images=lowercase__					)

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

           # add extra args
           __A		   =      processor(text=lowercase__				,				images=lowercase__				,				return_codebook_pixels=lowercase__				,				return_image_mask=lowercase__					)

           self.assertListEqual(
               list(inputs.keys()					)				,				[
                   "input_ids",
                   "token_type_ids",
                   "attention_mask",
                   "pixel_values",
                   "codebook_pixel_values",
                   "bool_masked_pos",
               ]				,				)

           # test if it raises when no input is passed
           with pytest.raises(lowercase__					):
                 processor()






     def     _UpperCamelCase   (self    :	int					)  ->       Optional[int]:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __A		   =      processor.batch_decode(lowercase__					)
           __A		   =      tokenizer.batch_decode(lowercase__					)

           self.assertListEqual(lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           __A		   =      self.get_image_processor()
           __A		   =      self.get_tokenizer()

           __A		   =      FlavaProcessor(tokenizer=lowercase__				,				image_processor=lowercase__					)

           __A		   =      "lower newer"
           __A		   =      self.prepare_image_inputs()

           __A		   =      processor(text=lowercase__				,				images=lowercase__					)

           self.assertListEqual(list(inputs.keys()					)				,				processor.model_input_names					)










'''simple docstring'''







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,
       )


__A      :       int      =	logging.get_logger(__name__)


__A      :       Optional[int]      =	Dict[str, Any]
__A      :       Any      =	List[Prediction]






@add_end_docstrings(lowerCamelCase						)
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           super().__init__(*lowercase__				,				**lowercase__					)

           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    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






     def __call__(self    :	int				,				lowercase__    :	Any				,				**lowercase__    :	Dict					)  ->       Union[Predictions, List[Prediction]]:
           return super().__call__(lowercase__				,				**lowercase__					)






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






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

           if fn is not None:
                 __A		   =      fn(
                     lowercase__				,				threshold=lowercase__				,				mask_threshold=lowercase__				,				overlap_mask_area_threshold=lowercase__				,				target_sizes=model_outputs["target_size"]				,				)[0]

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                       __A		   =      (segmentation == segment["id"]) * 255
                       __A		   =      Image.fromarray(mask.numpy().astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[segment["label_id"]]
                       __A		   =      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		   =      self.image_processor.post_process_semantic_segmentation(
                     lowercase__				,				target_sizes=model_outputs["target_size"]					)[0]

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

                 for label in labels:
                       __A		   =      (segmentation == label) * 255
                       __A		   =      Image.fromarray(mask.astype(np.uinta					)				,				mode="L"					)
                       __A		   =      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


