





import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
    import os

    from flax.core.frozen_dict import unfreeze
    from flax.traverse_util import flatten_dict

    from transformers import FlaxBertModel

    __lowerCamelCase	:					str			  =					"0.12"  # assumed parallelism: 8



@require_flax
@is_staging_test
class  A__       (	unittest.TestCase      ):





      @classmethod
      def 	__UpperCamelCase(			cls       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       TOKEN
       HfFolder.save_token(_SCREAMING_SNAKE_CASE       )





      @classmethod
      def 	__UpperCamelCase(			cls       ):
       '''simple docstring'''




       try:
        delete_repo(token=cls._token	,							repo_id="test-model-flax"       )
       except HTTPError:
        pass

       try:
        delete_repo(token=cls._token	,							repo_id="valid_org/test-model-flax-org"       )
       except HTTPError:
        pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       BertConfig(
           vocab_size=99	,							hidden_size=32	,							num_hidden_layers=5	,							num_attention_heads=4	,							intermediate_size=37       )
       UpperCamelCase     : Dict	     =       FlaxBertModel(_SCREAMING_SNAKE_CASE       )
       model.push_to_hub("test-model-flax"	,							use_auth_token=self._token       )

       UpperCamelCase     : Optional[Any]	     =       FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""       )

       UpperCamelCase     : Dict	     =       flatten_dict(unfreeze(model.params       )       )
       UpperCamelCase     : List[Any]	     =       flatten_dict(unfreeze(new_model.params       )       )

       for key in base_params.keys():
        UpperCamelCase     : Optional[int]	     =       (base_params[key] - new_params[key]).sum().item()
        self.assertLessEqual(_SCREAMING_SNAKE_CASE	,							1e-3	,							msg=F"""{key} not identical"""       )

       # Reset repo
       delete_repo(token=self._token	,							repo_id="test-model-flax"       )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
        model.save_pretrained(_SCREAMING_SNAKE_CASE	,							repo_id="test-model-flax"	,							push_to_hub=_SCREAMING_SNAKE_CASE	,							use_auth_token=self._token       )

       UpperCamelCase     : Dict	     =       FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""       )

       UpperCamelCase     : Tuple	     =       flatten_dict(unfreeze(model.params       )       )
       UpperCamelCase     : List[Any]	     =       flatten_dict(unfreeze(new_model.params       )       )

       for key in base_params.keys():
        UpperCamelCase     : Optional[Any]	     =       (base_params[key] - new_params[key]).sum().item()
        self.assertLessEqual(_SCREAMING_SNAKE_CASE	,							1e-3	,							msg=F"""{key} not identical"""       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       BertConfig(
           vocab_size=99	,							hidden_size=32	,							num_hidden_layers=5	,							num_attention_heads=4	,							intermediate_size=37       )
       UpperCamelCase     : List[str]	     =       FlaxBertModel(_SCREAMING_SNAKE_CASE       )
       model.push_to_hub("valid_org/test-model-flax-org"	,							use_auth_token=self._token       )

       UpperCamelCase     : Optional[Any]	     =       FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"       )

       UpperCamelCase     : Any	     =       flatten_dict(unfreeze(model.params       )       )
       UpperCamelCase     : str	     =       flatten_dict(unfreeze(new_model.params       )       )

       for key in base_params.keys():
        UpperCamelCase     : Any	     =       (base_params[key] - new_params[key]).sum().item()
        self.assertLessEqual(_SCREAMING_SNAKE_CASE	,							1e-3	,							msg=F"""{key} not identical"""       )

       # Reset repo
       delete_repo(token=self._token	,							repo_id="valid_org/test-model-flax-org"       )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
        model.save_pretrained(
            _SCREAMING_SNAKE_CASE	,							repo_id="valid_org/test-model-flax-org"	,							push_to_hub=_SCREAMING_SNAKE_CASE	,							use_auth_token=self._token       )

       UpperCamelCase     : Optional[int]	     =       FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"       )

       UpperCamelCase     : Any	     =       flatten_dict(unfreeze(model.params       )       )
       UpperCamelCase     : Dict	     =       flatten_dict(unfreeze(new_model.params       )       )

       for key in base_params.keys():
        UpperCamelCase     : int	     =       (base_params[key] - new_params[key]).sum().item()
        self.assertLessEqual(_SCREAMING_SNAKE_CASE	,							1e-3	,							msg=F"""{key} not identical"""       )



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 UpperCamelCase     : Optional[Any]	     =       True
 UpperCamelCase     : Union[str, Any]	     =       flatten_dict(modela.params					)
 UpperCamelCase     : Tuple	     =       flatten_dict(modela.params					)
 for key in flat_params_a.keys():
  if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]					)					) > 1e-4:
   UpperCamelCase     : List[str]	     =       False

 return models_are_equal



@require_flax
class  A__       (	unittest.TestCase      ):





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"       )
       UpperCamelCase     : Dict	     =       FlaxBertModel(_SCREAMING_SNAKE_CASE       )

       UpperCamelCase     : int	     =       """bert"""
       with tempfile.TemporaryDirectory() as tmp_dir:
        model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       )       )

        with self.assertRaises(_SCREAMING_SNAKE_CASE       ):
         UpperCamelCase     : Optional[int]	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE       )

        UpperCamelCase     : Tuple	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE	,							subfolder=_SCREAMING_SNAKE_CASE       )

       self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"       )
       UpperCamelCase     : Dict	     =       FlaxBertModel(_SCREAMING_SNAKE_CASE       )

       UpperCamelCase     : Optional[Any]	     =       """bert"""
       with tempfile.TemporaryDirectory() as tmp_dir:
        model.save_pretrained(os.path.join(_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       )	,							max_shard_size="10KB"       )

        with self.assertRaises(_SCREAMING_SNAKE_CASE       ):
         UpperCamelCase     : str	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE       )

        UpperCamelCase     : List[str]	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE	,							subfolder=_SCREAMING_SNAKE_CASE       )

       self.assertTrue(check_models_equal(_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       """bert"""
       UpperCamelCase     : Union[str, Any]	     =       """hf-internal-testing/tiny-random-bert-subfolder"""

       with self.assertRaises(_SCREAMING_SNAKE_CASE       ):
        UpperCamelCase     : Dict	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE       )

       UpperCamelCase     : Any	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE	,							subfolder=_SCREAMING_SNAKE_CASE       )

       self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       """bert"""
       UpperCamelCase     : int	     =       """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
       with self.assertRaises(_SCREAMING_SNAKE_CASE       ):
        UpperCamelCase     : int	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE       )

       UpperCamelCase     : Optional[Any]	     =       FlaxBertModel.from_pretrained(_SCREAMING_SNAKE_CASE	,							subfolder=_SCREAMING_SNAKE_CASE       )

       self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )







from pathlib import Path

import numpy as np
from PIL import Image



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 return (gray > 127) & (gray <= 255)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     : Dict	     =       np.zeros_like(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

 # Copy image to padded image
 UpperCamelCase     : Tuple	     =       image

 # Iterate over image & apply kernel
 for x in range(image.shape[1]					):
  for y in range(image.shape[0]					):
   UpperCamelCase     : List[str]	     =       (
       kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
   ).sum()
   UpperCamelCase     : int	     =       int(summation > 0					)
 return output


if __name__ == "__main__":
    # read original image
    __lowerCamelCase	:					int			  =					Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
    __lowerCamelCase	:					Tuple			  =					np.array(Image.open(lena_path))
    # kernel to be applied
    __lowerCamelCase	:					Optional[Any]			  =					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
    __lowerCamelCase	:					Union[str, Any]			  =					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
    # Save the output image
    __lowerCamelCase	:					Any			  =					Image.fromarray(output).convert("""RGB""")
    pil_img.save("""result_dilation.png""")
