
"""simple docstring"""




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__						:
      @staticmethod
      def 			__magic_name__			(			*_a	,    **_a				):
             pass


def   __magic_name__	(      __snake_case				:       Image  ) ->				str:
       lowercase    :				Tuple       		=							hashlib.mda(image.tobytes()  )
       return m.hexdigest()[:10]


def   __magic_name__	(      __snake_case				:       Image  ) ->				Dict:
       lowercase    :				Optional[Any]       		=							np.array(__snake_case  )
       lowercase    :				Any       		=							npimg.shape
       return {"hash": hashimage(__snake_case  ), "shape": shape}

@is_pipeline_test
@require_vision
@require_torch
class 		a__						(      unittest.TestCase						):
   __lowerCAmelCase            =				dict(
       (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
   __lowerCAmelCase            =				dict(
       (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							MaskGenerationPipeline(model=_a	,    image_processor=_a				)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
   def 			__magic_name__			(			self	,    _a	,    _a				):
          pass
   @require_tf
   @unittest.skip("Image segmentation not implemented in TF"				)
   def 			__magic_name__			(			self				):
          pass
   @slow
   @require_torch
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							pipeline("mask-generation"	,    model="facebook/sam-vit-huge"				)

          lowercase    :				int       		=							image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"	,    points_per_batch=256				)

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

          # fmt: off
          self.assertEqual(
              nested_simplify(_a	,    decimals=4				)	,    [
                  {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
                  {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
                  {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
                  {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
                  {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
                  {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
                  {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
                  {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
                  {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
                  {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
                  {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
                  {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
                  {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
                  {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
                  {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
                  {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
                  {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
                  {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
                  {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
                  {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
                  {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
                  {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
                  {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
                  {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
                  {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
                  {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
                  {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
                  {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
                  {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
                  {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
              ]	,    )
          # fmt: on

   @require_torch
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							"facebook/sam-vit-huge"
          lowercase    :				int       		=							pipeline("mask-generation"	,    model=_a				)

          lowercase    :				Union[str, Any]       		=							image_segmenter(
              "http://images.cocodataset.org/val2017/000000039769.jpg"	,    pred_iou_thresh=1	,    points_per_batch=256				)

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

          self.assertEqual(
              nested_simplify(_a	,    decimals=4				)	,    [
                  {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
                  {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
                  {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
                  {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
                  {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
              ]	,    )




"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   _A			:       Union[str, Any]   		=       None

_A			:       str   		=       logging.get_logger(__name__)
_A			:       Tuple   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Optional[Any]   		=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_A			:       Optional[Any]   		=       {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_A			:       List[str]   		=       """▁"""

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase            =				FNetTokenizer
   def __init__(			self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=True	,    _a="<unk>"	,    _a="[SEP]"	,    _a="<pad>"	,    _a="[CLS]"	,    _a="[MASK]"	,    **_a	,    ):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          lowercase    :				int       		=							(
              AddedToken(_a	,    lstrip=_a	,    rstrip=_a	,    normalized=_a				)
              if isinstance(_a	,    _a				)
              else mask_token
          )

          super().__init__(
              _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    **_a	,    )

          lowercase    :				Dict       		=							do_lower_case
          lowercase    :				Union[str, Any]       		=							remove_space
          lowercase    :				Any       		=							keep_accents
          lowercase    :				List[Any]       		=							vocab_file
          lowercase    :				Union[str, Any]       		=							False if not self.vocab_file else True
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[Any]       		=							[self.sep_token_id]
          lowercase    :				str       		=							[self.cls_token_id]
          if token_ids_a is None:
                 return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Any       		=							[self.sep_token_id]
          lowercase    :				Dict       		=							[self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file	,    _a				)

          return (out_vocab_file,)


