





import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class      A_	(tf.keras.layers.Layer      ):
	def __init__(   self							, _A							, _A							, _A = None							, _A = None							, _A = None							, _A = "longest"							, _A = True							, _A = 5_1_2							, _A = None							, _A = True							, _A = True							, _A = True							, **_A							, ):






				'''simple docstring'''







				super().__init__()
				if use_fast_bert_tokenizer:
							UpperCAmelCase       		=    FastBertTokenizer(
							    SCREAMING_SNAKE_CASE__							, token_out_type=tf.intaa							, lower_case_nfd_strip_accents=SCREAMING_SNAKE_CASE__							, **SCREAMING_SNAKE_CASE__		)
				else:
							UpperCAmelCase       		=    tf.lookup.StaticVocabularyTable(
							    tf.lookup.KeyValueTensorInitializer(
							        keys=SCREAMING_SNAKE_CASE__							, key_dtype=tf.string							, values=tf.range(tf.size(SCREAMING_SNAKE_CASE__							, out_type=tf.intaa		)							, dtype=tf.intaa		)							, value_dtype=tf.intaa							, )							, num_oov_buckets=1							, )
							UpperCAmelCase       		=    BertTokenizerLayer(
							    SCREAMING_SNAKE_CASE__							, token_out_type=tf.intaa							, lower_case=SCREAMING_SNAKE_CASE__							, **SCREAMING_SNAKE_CASE__		)

				UpperCAmelCase       		=    vocab_list
				UpperCAmelCase       		=    do_lower_case
				UpperCAmelCase       		=    cls_token_id or vocab_list.index('''[CLS]'''		)
				UpperCAmelCase       		=    sep_token_id or vocab_list.index('''[SEP]'''		)
				UpperCAmelCase       		=    pad_token_id or vocab_list.index('''[PAD]'''		)
				UpperCAmelCase       		=    ShrinkLongestTrimmer(max_length - 3							, axis=1		)  # Allow room for special tokens
				UpperCAmelCase       		=    max_length
				UpperCAmelCase       		=    padding
				UpperCAmelCase       		=    truncation
				UpperCAmelCase       		=    pad_to_multiple_of
				UpperCAmelCase       		=    return_token_type_ids
				UpperCAmelCase       		=    return_attention_mask
	@classmethod
	def 					_lowercase    (   cls							, _A							, **_A		):  # noqa: F821






				'''simple docstring'''







				UpperCAmelCase       		=    kwargs.pop('''do_lower_case'''							, SCREAMING_SNAKE_CASE__		)
				UpperCAmelCase       		=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
				UpperCAmelCase       		=    kwargs.pop('''cls_token_id'''							, SCREAMING_SNAKE_CASE__		)
				UpperCAmelCase       		=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
				UpperCAmelCase       		=    kwargs.pop('''sep_token_id'''							, SCREAMING_SNAKE_CASE__		)
				UpperCAmelCase       		=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
				UpperCAmelCase       		=    kwargs.pop('''pad_token_id'''							, SCREAMING_SNAKE_CASE__		)
				UpperCAmelCase       		=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

				UpperCAmelCase       		=    tokenizer.get_vocab()
				UpperCAmelCase       		=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]							, key=lambda _A		: x[1]		)
				UpperCAmelCase       		=    [entry[0] for entry in vocab]
				return cls(
				    vocab_list=SCREAMING_SNAKE_CASE__							, do_lower_case=SCREAMING_SNAKE_CASE__							, cls_token_id=SCREAMING_SNAKE_CASE__							, sep_token_id=SCREAMING_SNAKE_CASE__							, pad_token_id=SCREAMING_SNAKE_CASE__							, **SCREAMING_SNAKE_CASE__							, )
	@classmethod
	def 					_lowercase    (   cls							, _A							, *_A							, **_A		):






				'''simple docstring'''







				try:
							UpperCAmelCase       		=    BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							, *SCREAMING_SNAKE_CASE__							, **SCREAMING_SNAKE_CASE__		)
				except:  # noqa: E722
							from .tokenization_bert_fast import BertTokenizerFast

							UpperCAmelCase       		=    BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__							, *SCREAMING_SNAKE_CASE__							, **SCREAMING_SNAKE_CASE__		)
				return cls.from_tokenizer(SCREAMING_SNAKE_CASE__							, **SCREAMING_SNAKE_CASE__		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				if self.do_lower_case:
							UpperCAmelCase       		=    case_fold_utfa(SCREAMING_SNAKE_CASE__		)
				UpperCAmelCase       		=    self.tf_tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
				return tokens.merge_dims(1							, -1		)
	def 					_lowercase    (   self							, _A							, _A=None							, _A=None							, _A=None							, _A=None							, _A=None							, _A=None							, _A=None							, ):






				'''simple docstring'''







				if padding is None:
							UpperCAmelCase       		=    self.padding
				if padding not in ("longest", "max_length"):
							raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''		)
				if max_length is not None and text_pair is not None:
							# Because we have to instantiate a Trimmer to do it properly
							raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''		)
				if max_length is None:
							UpperCAmelCase       		=    self.max_length
				if truncation is None:
							UpperCAmelCase       		=    self.truncation
				if pad_to_multiple_of is None:
							UpperCAmelCase       		=    self.pad_to_multiple_of
				if return_token_type_ids is None:
							UpperCAmelCase       		=    self.return_token_type_ids
				if return_attention_mask is None:
							UpperCAmelCase       		=    self.return_attention_mask
				if not isinstance(SCREAMING_SNAKE_CASE__							, tf.Tensor		):
							UpperCAmelCase       		=    tf.convert_to_tensor(SCREAMING_SNAKE_CASE__		)
				if text_pair is not None and not isinstance(SCREAMING_SNAKE_CASE__							, tf.Tensor		):
							UpperCAmelCase       		=    tf.convert_to_tensor(SCREAMING_SNAKE_CASE__		)
				if text_pair is not None:
							if text.shape.rank > 1:
										raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''		)
							if text_pair.shape.rank > 1:
										raise ValueError('''text_pair should not be multidimensional!'''		)
				if text.shape.rank == 2:
							UpperCAmelCase       		=    text[:, 0], text[:, 1]
				UpperCAmelCase       		=    self.unpaired_tokenize(SCREAMING_SNAKE_CASE__		)
				if text_pair is None:  # Unpaired text
							if truncation:
										UpperCAmelCase       		=    text[:, : max_length - 2]  # Allow room for special tokens
							UpperCAmelCase       		=    combine_segments(
							    (text,)							, start_of_sequence_id=self.cls_token_id							, end_of_segment_id=self.sep_token_id		)
				else:  # Paired text
							UpperCAmelCase       		=    self.unpaired_tokenize(SCREAMING_SNAKE_CASE__		)
							if truncation:
										UpperCAmelCase       		=    self.paired_trimmer.trim([text, text_pair]		)
							UpperCAmelCase       		=    combine_segments(
							    (text, text_pair)							, start_of_sequence_id=self.cls_token_id							, end_of_segment_id=self.sep_token_id		)
				if padding == "longest":
							UpperCAmelCase       		=    input_ids.bounding_shape(axis=1		)
							if pad_to_multiple_of is not None:
										# No ceiling division in tensorflow, so we negate floordiv instead
										UpperCAmelCase       		=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length							, SCREAMING_SNAKE_CASE__		))
				else:
							UpperCAmelCase       		=    max_length

				UpperCAmelCase       		=    pad_model_inputs(SCREAMING_SNAKE_CASE__							, max_seq_length=SCREAMING_SNAKE_CASE__							, pad_value=self.pad_token_id		)
				UpperCAmelCase       		=    {"input_ids": input_ids}
				if return_attention_mask:
							UpperCAmelCase       		=    attention_mask
				if return_token_type_ids:
							UpperCAmelCase       		=    pad_model_inputs(
							    SCREAMING_SNAKE_CASE__							, max_seq_length=SCREAMING_SNAKE_CASE__							, pad_value=self.pad_token_id		)
							UpperCAmelCase       		=    token_type_ids
				return output







	def 					_lowercase    (   self		):






				'''simple docstring'''







				return {
				    "vocab_list": self.vocab_list,
				    "do_lower_case": self.do_lower_case,
				    "cls_token_id": self.cls_token_id,
				    "sep_token_id": self.sep_token_id,
				    "pad_token_id": self.pad_token_id,
				}










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.ndarray:







			'''simple docstring'''

			UpperCAmelCase       		=    np.stack((horizontal_flow, vertical_flow)      ,      2			)

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

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

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

			# Set pixels at invalid locations to 0
			UpperCAmelCase       		=    0

			return warped





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = None      ,      )  ->    tuple[np.ndarray, np.ndarray]:







			'''simple docstring'''

			if alpha is None:
						UpperCAmelCase       		=    0.1

			# Initialize flow
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)

			# Prepare kernels for the calculation of the derivatives and the average velocity
			UpperCAmelCase       		=    np.array([[-1, 1], [-1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[-1, -1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[1, 1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    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(UpperCamelCase__			):
						UpperCAmelCase       		=    warp(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      -kernel_t			)

						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)

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

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

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()



