
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
    import torch




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """char"""
  SCREAMING_SNAKE_CASE__         =      """bpe"""
  SCREAMING_SNAKE_CASE__         =      """wp"""






lowerCamelCase_						     =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      ["""image_processor""", """char_tokenizer"""]
  SCREAMING_SNAKE_CASE__         =      """ViTImageProcessor"""
  SCREAMING_SNAKE_CASE__         =      """MgpstrTokenizer"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    None
       if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""	,      _snake_case	,      )
            UpperCamelCase__				=    kwargs.pop("""feature_extractor"""						)

       UpperCamelCase__				=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""						)
       if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""						)

       UpperCamelCase__				=    tokenizer
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""gpt2"""						)
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""bert-base-uncased"""						)

       super().__init__(_snake_case	,      _snake_case						)







  def __call__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):




       if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

       if images is not None:
            UpperCamelCase__				=    self.image_processor(_snake_case	,      return_tensors=_snake_case	,      **_snake_case						)
       if text is not None:
            UpperCamelCase__				=    self.char_tokenizer(_snake_case	,      return_tensors=_snake_case	,      **_snake_case						)

       if text is None:
            return inputs
       elif images is None:
            return encodings
       else:
            UpperCamelCase__				=    encodings["""input_ids"""]
            return inputs







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    sequences
       UpperCamelCase__				=    char_preds.size(0						)

       UpperCamelCase__		,	UpperCamelCase__				=    self._decode_helper(_snake_case	,      """char"""						)
       UpperCamelCase__		,	UpperCamelCase__				=    self._decode_helper(_snake_case	,      """bpe"""						)
       UpperCamelCase__		,	UpperCamelCase__				=    self._decode_helper(_snake_case	,      """wp"""						)

       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       for i in range(_snake_case						):
            UpperCamelCase__				=    [char_scores[i], bpe_scores[i], wp_scores[i]]
            UpperCamelCase__				=    [char_strs[i], bpe_strs[i], wp_strs[i]]
            UpperCamelCase__				=    scores.index(max(_snake_case						)						)
            final_strs.append(strs[max_score_index]						)
            final_scores.append(scores[max_score_index]						)

       UpperCamelCase__				=    {}
       UpperCamelCase__				=    final_strs
       UpperCamelCase__				=    final_scores
       UpperCamelCase__				=    char_strs
       UpperCamelCase__				=    bpe_strs
       UpperCamelCase__				=    wp_strs
       return out







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):




       if format == DecodeType.CHARACTER:
            UpperCamelCase__				=    self.char_decode
            UpperCamelCase__				=    1
            UpperCamelCase__				=    """[s]"""
       elif format == DecodeType.BPE:
            UpperCamelCase__				=    self.bpe_decode
            UpperCamelCase__				=    2
            UpperCamelCase__				=    """#"""
       elif format == DecodeType.WORDPIECE:
            UpperCamelCase__				=    self.wp_decode
            UpperCamelCase__				=    1_02
            UpperCamelCase__				=    """[SEP]"""
       else:
            raise ValueError(F"Format {format} is not supported."						)

       UpperCamelCase__		,	UpperCamelCase__				=    [], []
       UpperCamelCase__				=    pred_logits.size(0						)
       UpperCamelCase__				=    pred_logits.size(1						)
       UpperCamelCase__		,	UpperCamelCase__				=    pred_logits.topk(1	,      dim=-1	,      largest=_snake_case	,      sorted=_snake_case						)
       UpperCamelCase__				=    preds_index.view(-1	,      _snake_case						)[:, 1:]
       UpperCamelCase__				=    decoder(_snake_case						)
       UpperCamelCase__		,	UpperCamelCase__				=    torch.nn.functional.softmax(_snake_case	,      dim=2						).max(dim=2						)
       UpperCamelCase__				=    preds_max_prob[:, 1:]

       for index in range(_snake_case						):
            UpperCamelCase__				=    preds_str[index].find(_snake_case						)
            UpperCamelCase__				=    preds_str[index][:pred_eos]
            UpperCamelCase__				=    preds_index[index].cpu().tolist()
            UpperCamelCase__				=    pred_index.index(_snake_case						) if eos_token in pred_index else -1
            UpperCamelCase__				=    preds_max_prob[index][: pred_eos_index + 1]
            UpperCamelCase__				=    pred_max_prob.cumprod(dim=0						)[-1] if pred_max_prob.nelement() != 0 else 0.0
            dec_strs.append(_snake_case						)
            conf_scores.append(_snake_case						)

       return dec_strs, conf_scores







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    [seq.replace(""" """	,      """"""						) for seq in self.char_tokenizer.batch_decode(_snake_case						)]
       return decode_strs







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       return self.bpe_tokenizer.batch_decode(_snake_case						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    [seq.replace(""" """	,      """"""						) for seq in self.wp_tokenizer.batch_decode(_snake_case						)]
       return decode_strs







import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:




    class      __A:

      """simple docstring"""







      @staticmethod
      def 	UpperCAmelCase_ (*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
           pass






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__				=    hashlib.mda(image.tobytes()						)
     return m.hexdigest()[:10]






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




     UpperCamelCase__				=    np.array(__a						)
     UpperCamelCase__				=    npimg.shape
     return {"hash": hashimage(__a						), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  SCREAMING_SNAKE_CASE__         =      dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       pass







  @require_tf
  @unittest.skip("""Image segmentation not implemented in TF"""						)
  def 	UpperCAmelCase_ (self						):
       pass







  @slow
  @require_torch
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    pipeline("""mask-generation"""	,      model="""facebook/sam-vit-huge"""						)

       UpperCamelCase__				=    image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,      points_per_batch=2_56						)

       # Shortening by hashing
       UpperCamelCase__				=    []
       for i, o in enumerate(outputs["""masks"""]						):
            new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_						), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871}
           ]	,      )
       # fmt: on







  @require_torch
  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """facebook/sam-vit-huge"""
       UpperCamelCase__				=    pipeline("""mask-generation"""	,      model=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""	,      pred_iou_thresh=1	,      points_per_batch=2_56						)

       # Shortening by hashing
       UpperCamelCase__				=    []
       for i, o in enumerate(outputs["""masks"""]						):
            new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_						), "scores": outputs["scores"][i]}]

       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
           ]	,      )





