
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       __A(							unittest.TestCase					):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__            =     ViTImageProcessor if is_vision_available() else None







  @property
  def 			UpperCAmelCase_   (self		):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 			UpperCAmelCase_   (self		):
       UpperCamelCase__												=							(3, 32, 1_28)
       UpperCamelCase__												=							tempfile.mkdtemp()

       # fmt: off
       UpperCamelCase__												=							["""[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
       UpperCamelCase__												=							dict(zip(a_			,     range(len(a_		)		)		)		)

       UpperCamelCase__												=							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(a_		) + """\n"""		)

       UpperCamelCase__												=							{
           """do_normalize""": False,
           """do_resize""": True,
           """image_processor_type""": """ViTImageProcessor""",
           """resample""": 3,
           """size""": {"""height""": 32, """width""": 1_28},
       }
       UpperCamelCase__												=							os.path.join(self.tmpdirname			,     a_		)
       with open(self.image_processor_file			,     """w"""			,     encoding="""utf-8"""		) as fp:
            json.dump(a_			,     a_		)







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







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







  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(a_			,     0			,     -1		)		)

       return image_input







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

       UpperCamelCase__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)
       processor.save_pretrained(self.tmpdirname		)
       UpperCamelCase__												=							MgpstrProcessor.from_pretrained(self.tmpdirname			,     use_fast=a_		)

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

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







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

       UpperCamelCase__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)
       processor.save_pretrained(self.tmpdirname		)

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

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

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

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







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

       UpperCamelCase__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)

       UpperCamelCase__												=							self.prepare_image_inputs()

       UpperCamelCase__												=							image_processor(a_			,     return_tensors="""np"""		)
       UpperCamelCase__												=							processor(images=a_			,     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__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)

       UpperCamelCase__												=							"""test"""

       UpperCamelCase__												=							processor(text=a_		)

       UpperCamelCase__												=							tokenizer(a_		)
       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__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)

       UpperCamelCase__												=							"""test"""
       UpperCamelCase__												=							self.prepare_image_inputs()

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

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

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







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

       UpperCamelCase__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)

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

       UpperCamelCase__												=							processor.char_decode(a_		)
       UpperCamelCase__												=							tokenizer.batch_decode(a_		)
       UpperCamelCase__												=							[seq.replace(""" """			,     """"""		) for seq in decoded_tok]

       self.assertListEqual(a_			,     a_		)







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

       UpperCamelCase__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)

       UpperCamelCase__												=							None
       UpperCamelCase__												=							self.prepare_image_inputs()

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

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







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

       UpperCamelCase__												=							MgpstrProcessor(tokenizer=a_			,     image_processor=a_		)

       UpperCamelCase__												=							torch.randn(1			,     27			,     38		)
       UpperCamelCase__												=							torch.randn(1			,     27			,     5_02_57		)
       UpperCamelCase__												=							torch.randn(1			,     27			,     3_05_22		)

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

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








'''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 							lowercase_						:

    '''simple docstring'''





    @staticmethod
    def     snake_case_      (  *a_ ,			**a_  )				->					Tuple:


      """simple docstring"""
      pass


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						hashlib.mda(image.tobytes()    )
  return m.hexdigest()[:10]


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						np.array(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						npimg.shape
  return {"hash": hashimage(SCREAMING_SNAKE_CASE    ), "shape": shape}



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

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  __lowerCAmelCase       :			Tuple     	= dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						MaskGenerationPipeline(model=a_ ,			image_processor=a_  )
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]
  def     snake_case_      (  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    pass
  @require_tf
  @unittest.skip('Image segmentation not implemented in TF'  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    pass
  @slow
  @require_torch
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model='facebook/sam-vit-huge'  )

    UpperCAmelCase    				=						image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    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': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
            {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9967},
            {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
            {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9909},
            {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9879},
            {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9834},
            {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9716},
            {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9612},
            {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9599},
            {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9552},
            {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9532},
            {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9516},
            {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9499},
            {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9483},
            {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9464},
            {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9408},
            {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9335},
            {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9326},
            {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9262},
            {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8999},
            {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8986},
            {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8984},
            {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8873},
            {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8871}
        ] ,			)
    # fmt: on





  @require_torch
  @slow
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/sam-vit-huge'
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model=a_  )

    UpperCAmelCase    				=						image_segmenter(
        'http://images.cocodataset.org/val2017/000000039769.jpg' ,			pred_iou_thresh=1 ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    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': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0210},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
        ] ,			)


