



from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve

def       A_   (						__a  :					np.ndarray			,		__a  :					np.ndarray			,		__a  :					np.ndarray		):

				"""simple docstring"""
				a__					   =		np.stack((horizontal_flow, vertical_flow)			,		2		)

				# Create a grid of all pixel coordinates and subtract the flow to get the
				# target pixels coordinates
				a__					   =		np.stack(
				    np.meshgrid(np.arange(0			,		image.shape[1]		)			,		np.arange(0			,		image.shape[0]		)		)			,		2		)
				a__					   =		np.round(grid - flow		).astype(np.intaa		)

				# Find the locations outside of the original image
				a__					   =		(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]		))
				a__					   =		0

				a__					   =		image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				a__					   =		0

				return warped

def       A_   (						__a  :					np.ndarray			,		__a  :					np.ndarray			,		__a  :					SupportsIndex			,		__a  :					float | None = None			,		):

				"""simple docstring"""
				if alpha is None:
								a__					   =		0.1

				# Initialize flow
				a__					   =		np.zeros_like(__a		)
				a__					   =		np.zeros_like(__a		)

				# Prepare kernels for the calculation of the derivatives and the average velocity
				a__					   =		np.array([[-1, 1], [-1, 1]]		) * 0.2_5
				a__					   =		np.array([[-1, -1], [1, 1]]		) * 0.2_5
				a__					   =		np.array([[1, 1], [1, 1]]		) * 0.2_5
				a__					   =		np.array(
				    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]		)

				# Iteratively refine the flow
				for _ in range(__a		):
								a__					   =		warp(__a			,		__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		-kernel_t		)

								a__					   =		convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		)

								# This updates the flow as proposed in the paper (Step 12)
								a__					   =		(
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								a__					   =		update / (alpha**2 + derivative_x**2 + derivative_y**2)

								a__					   =		avg_horizontal_velocity - derivative_x * update
								a__					   =		avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
	import doctest

	doctest.testmod()






import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		UpperCAmelCase    (    snake_case_							,unittest.TestCase					):
   SCREAMING_SNAKE_CASE__					      =  PreTrainedTokenizerFast
   SCREAMING_SNAKE_CASE__					      =  False
   SCREAMING_SNAKE_CASE__					      =  True
   SCREAMING_SNAKE_CASE__					      =  '''tokenizer_file'''





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	False  # because we don't have pretrained_vocab_files_map
         super().setUp()
         _lowerCAmelCase			      =	True

         _lowerCAmelCase			      =	['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
         _lowerCAmelCase			      =	'''SaulLu/dummy-tokenizer-bytelevel-bpe'''

         # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
         _lowerCAmelCase			      =	[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

         _lowerCAmelCase			      =	PreTrainedTokenizerFast.from_pretrained(model_paths[0]      )
         tokenizer.save_pretrained(self.tmpdirname      )





   def 						__lowerCAmelCase				(						self      ):
         # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
         # model
         pass





   def 						__lowerCAmelCase				(						self      ):
         # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
         # model
         pass





   def 						__lowerCAmelCase				(						self      ):
         # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
         # model
         pass





   def 						__lowerCAmelCase				(						self      ):
         # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
         pass





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.tmpdirname
         # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                     try:
                           _lowerCAmelCase			      =	tempfile.mkdtemp()
                           _lowerCAmelCase			      =	self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase							,  **_lowerCAmelCase      )

                           tokenizer.save_pretrained(self.tmpdirname      )
                           super().test_training_new_tokenizer()
                     finally:
                           # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                           # is restored
                           shutil.rmtree(self.tmpdirname      )
                           _lowerCAmelCase			      =	tmpdirname_orig





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.tmpdirname
         # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                     try:
                           _lowerCAmelCase			      =	tempfile.mkdtemp()
                           _lowerCAmelCase			      =	self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase							,  **_lowerCAmelCase      )

                           tokenizer.save_pretrained(self.tmpdirname      )
                           super().test_training_new_tokenizer_with_special_tokens_change()
                     finally:
                           # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                           # is restored
                           shutil.rmtree(self.tmpdirname      )
                           _lowerCAmelCase			      =	tmpdirname_orig





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name      )

         _lowerCAmelCase			      =	('''a''' for _ in range(1_000      ))
         _lowerCAmelCase			      =	tokenizer.train_new_from_iterator(text_iterator=_lowerCAmelCase							,  length=1_000							,  vocab_size=50      )

         _lowerCAmelCase			      =	new_tokenizer.encode('''a🤗'''      )
         self.assertEqual(_lowerCAmelCase							,  [64, 172, 253, 97, 245]      )






@require_tokenizers
class 		UpperCAmelCase    (    unittest.TestCase					):





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	AutoTokenizer.from_pretrained('''bert-base-cased'''      )
         _lowerCAmelCase			      =	json.loads(tokenizer._tokenizer.to_str()      )
         _lowerCAmelCase			      =	len(_lowerCAmelCase      )

         with tempfile.TemporaryDirectory() as tmp_dir:
               # Hack to save this in the tokenizer_config.json
               _lowerCAmelCase			      =	['''tokenizer.4.0.0.json''']
               tokenizer.save_pretrained(_lowerCAmelCase      )
               json.dump(_lowerCAmelCase							,  open(os.path.join(_lowerCAmelCase							,  '''tokenizer.4.0.0.json'''      )							,  '''w'''      )      )

               # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
               _lowerCAmelCase			      =	AutoTokenizer.from_pretrained(_lowerCAmelCase      )
               self.assertEqual(len(_lowerCAmelCase      )							,  len(_lowerCAmelCase      ) + 1      )
               _lowerCAmelCase			      =	json.loads(new_tokenizer._tokenizer.to_str()      )
               self.assertIn('''huggingface'''							,  json_tokenizer['''model''']['''vocab''']      )

               # Will need to be adjusted if we reach v42 and this test is still here.
               # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
               shutil.move(os.path.join(_lowerCAmelCase							,  '''tokenizer.4.0.0.json'''      )							,  os.path.join(_lowerCAmelCase							,  '''tokenizer.42.0.0.json'''      )      )
               _lowerCAmelCase			      =	['''tokenizer.42.0.0.json''']
               tokenizer.save_pretrained(_lowerCAmelCase      )
               _lowerCAmelCase			      =	AutoTokenizer.from_pretrained(_lowerCAmelCase      )
               self.assertEqual(len(_lowerCAmelCase      )							,  len(_lowerCAmelCase      )      )
               _lowerCAmelCase			      =	json.loads(new_tokenizer._tokenizer.to_str()      )
               self.assertNotIn('''huggingface'''							,  json_tokenizer['''model''']['''vocab''']      )





   def 						__lowerCAmelCase				(						self      ):
         # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
         _lowerCAmelCase			      =	'''hf-internal-testing/test-two-tokenizers'''

         # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
         _lowerCAmelCase			      =	AutoTokenizer.from_pretrained(_lowerCAmelCase      )
         self.assertEqual(len(_lowerCAmelCase      )							,  28_997      )
         _lowerCAmelCase			      =	json.loads(tokenizer._tokenizer.to_str()      )
         self.assertIn('''huggingface'''							,  json_tokenizer['''model''']['''vocab''']      )

         # Testing an older version by monkey-patching the version in the module it's used.
         import transformers as old_transformers

         _lowerCAmelCase			      =	'''3.0.0'''
         _lowerCAmelCase			      =	old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCAmelCase      )
         self.assertEqual(len(_lowerCAmelCase      )							,  28_996      )
         _lowerCAmelCase			      =	json.loads(old_tokenizer._tokenizer.to_str()      )
         self.assertNotIn('''huggingface'''							,  json_tokenizer['''model''']['''vocab''']      )






@require_tokenizers
class 		UpperCAmelCase    (    unittest.TestCase					):





   def 						__lowerCAmelCase				(						self      ):
         # See https://github.com/huggingface/transformers/pull/12550
         # and https://github.com/huggingface/tokenizers/issues/537
         _lowerCAmelCase			      =	PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''      )
         _lowerCAmelCase			      =	'''The Matrix is a 1999 science fiction action film.'''

         with concurrent.futures.ThreadPoolExecutor() as executor:
               _lowerCAmelCase			      =	[executor.submit(self.fetch							,  _lowerCAmelCase							,  _lowerCAmelCase      ) for i in range(10      )]
               _lowerCAmelCase			      =	[future.result() for future in futures]
               self.assertEqual(_lowerCAmelCase							,  [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10      )]      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         return tokenizer.encode(_lowerCAmelCase							,  truncation='''longest_first'''							,  padding='''longest'''      )