
import json
import os
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 AlignProcessor, EfficientNetImageProcessor




@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    tempfile.mkdtemp()

       UpperCamelCase__				=    [
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       UpperCamelCase__				=    os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["""vocab_file"""]						)
       with open(self.vocab_file	,      """w"""	,      encoding="""utf-8"""						) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

       UpperCamelCase__				=    {
           """do_resize""": True,
           """size""": 20,
           """do_center_crop""": True,
           """crop_size""": 18,
           """do_normalize""": True,
           """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
           """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
       }
       UpperCamelCase__				=    os.path.join(self.tmpdirname	,      SCREAMING_SNAKE_CASE_						)
       with open(self.image_processor_file	,      """w"""	,      encoding="""utf-8"""						) as fp:
            json.dump(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return BertTokenizer.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return BertTokenizerFast.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						)







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    [np.random.randint(2_55	,      size=(3, 30, 4_00)	,      dtype=np.uinta						)]
       UpperCamelCase__				=    [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_	,      0	,      -1						)						) for x in image_inputs]
       return image_inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_rust_tokenizer()
       UpperCamelCase__				=    self.get_image_processor()

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)
       processor_slow.save_pretrained(self.tmpdirname						)
       UpperCamelCase__				=    AlignProcessor.from_pretrained(self.tmpdirname	,      use_fast=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)
       processor_fast.save_pretrained(self.tmpdirname						)
       UpperCamelCase__				=    AlignProcessor.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	,      SCREAMING_SNAKE_CASE_						)
       self.assertIsInstance(processor_fast.tokenizer	,      SCREAMING_SNAKE_CASE_						)

       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	,      SCREAMING_SNAKE_CASE_						)
       self.assertIsInstance(processor_fast.image_processor	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AlignProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

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

       UpperCamelCase__				=    AlignProcessor.from_pretrained(
           self.tmpdirname	,      bos_token="""(BOS)"""	,      eos_token="""(EOS)"""	,      do_normalize=SCREAMING_SNAKE_CASE_	,      padding_value=1.0						)

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)
       UpperCamelCase__				=    processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      padding="""max_length"""	,      max_length=64						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    processor.batch_decode(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

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







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},
           ]	,      )





