
"""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  lowerCamelCase_						(							*_lowerCamelCase :Optional[Any]      ,  **_lowerCamelCase :int   ):







       '''simple docstring'''



       pass




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=hashlib.mda(image.tobytes()	)
 return m.hexdigest()[:10]




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Optional[int]   	=np.array(SCREAMING_SNAKE_CASE_	)
 UpperCamelCase_     :					int   	=npimg.shape
 return {"hash": hashimage(SCREAMING_SNAKE_CASE_	), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 				a__					(						unittest.TestCase       ):
     UpperCAmelCase__         =      dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
     UpperCAmelCase__         =      dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=MaskGenerationPipeline(model=_lowercase      ,  image_processor=_lowercase   )
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      pass



     @require_tf
     @unittest.skip('Image segmentation not implemented in TF'   )
     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      pass



     @slow
     @require_torch
     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=pipeline('mask-generation'      ,  model='facebook/sam-vit-huge'   )

      UpperCamelCase_     :					Optional[Any]   	=image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'      ,  points_per_batch=256   )

      # Shortening by hashing
      UpperCamelCase_     :					Union[str, Any]   	=[]
      for i, o in enumerate(outputs['masks']   ):
       new_outupt += [{"mask": mask_to_test_readable(_lowercase   ), "scores": outputs["scores"][i]}]

      # fmt: off
      self.assertEqual(
          nested_simplify(_lowercase      ,  decimals=4   )      ,  [
              {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
              {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
              {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
              {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
              {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
              {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
              {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
              {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
              {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
              {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
              {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
              {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
              {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
              {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
              {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
              {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
              {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
              {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
              {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
              {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
              {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
              {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
              {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
              {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
              {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
              {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
          ]      ,  )
      # fmt: on




     @require_torch
     @slow
     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	='facebook/sam-vit-huge'
      UpperCamelCase_     :					Dict   	=pipeline('mask-generation'      ,  model=_lowercase   )

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

      # Shortening by hashing
      UpperCamelCase_     :					Optional[Any]   	=[]
      for i, o in enumerate(outputs['masks']   ):
       new_outupt += [{"mask": mask_to_test_readable(_lowercase   ), "scores": outputs["scores"][i]}]

      self.assertEqual(
          nested_simplify(_lowercase      ,  decimals=4   )      ,  [
              {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
          ]      ,  )






'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ViTImageProcessor if is_vision_available() else None



					@property
					def     __snake_case						(	self     :						List[str])    ->							Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Optional[Any])    ->							List[str]:
								A_									= (3, 32, 128)
								A_									= tempfile.mkdtemp()

								# fmt: off
								A_									= ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
								# fmt: on
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')

								A_									= {
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 32, 'width': 128},
								}
								A_									= os.path.join(self.tmpdirname      ,       _lowercase)
								with open(self.image_processor_file      ,       'w'      ,       encoding='utf-8') as fp:
											json.dump(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						int      ,       **_lowercase     :						Optional[int])    ->							int:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						Dict)    ->							str:
								shutil.rmtree(self.tmpdirname)



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

								A_									= Image.fromarray(np.moveaxis(_lowercase      ,       0      ,       -1))

								return image_input



					def     __snake_case						(	self     :						Optional[Any])    ->							List[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								processor.save_pretrained(self.tmpdirname)
								A_									= MgpstrProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_lowercase)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

								self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor.to_json_string())
								self.assertIsInstance(processor.image_processor      ,       _lowercase)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[Any]:
								A_									= self.get_tokenizer()
								A_									= self.get_image_processor()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)
								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_									= MgpstrProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=_lowercase      ,       padding_value=1.0)

								self.assertEqual(processor.char_tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.char_tokenizer      ,       _lowercase)

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



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

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



					def     __snake_case						(	self     :						Any)    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'

								A_									= processor(text=_lowercase)

								A_									= tokenizer(_lowercase)
								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key])



					def     __snake_case						(	self     :						str)    ->							Dict:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= 'test'
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

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

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



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

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

								A_									= processor.char_decode(_lowercase)
								A_									= tokenizer.batch_decode(_lowercase)
								A_									= [seq.replace(' '      ,       '') for seq in decoded_tok]

								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						List[str])    ->							str:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= None
								A_									= self.prepare_image_inputs()

								A_									= processor(text=_lowercase      ,       images=_lowercase)

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



					def     __snake_case						(	self     :						List[str])    ->							Any:
								A_									= self.get_image_processor()
								A_									= self.get_tokenizer()

								A_									= MgpstrProcessor(tokenizer=_lowercase      ,       image_processor=_lowercase)

								A_									= torch.randn(1      ,       27      ,       38)
								A_									= torch.randn(1      ,       27      ,       50_257)
								A_									= torch.randn(1      ,       27      ,       30_522)

								A_									= processor.batch_decode([char_input, bpe_input, wp_input])

								self.assertListEqual(list(results.keys())      ,       ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'])




