



'''simple docstring'''

import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

a							   =				imread(r'digital_image_processing/image_data/lena_small.jpg')
a							   =				cvtColor(img, COLOR_BGR2GRAY)







def a_     (    )				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=cn.convert_to_negative(__UpperCAmelCase					)
			# assert negative_img array for at least one True
			assert negative_img.any()







def a_     (    )				->					List[Any]:





			"""simple docstring"""







			with Image.open('digital_image_processing/image_data/lena_small.jpg'					) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(__UpperCAmelCase				,							1_10					)					).startswith(
						    '<PIL.Image.Image image mode=RGB size=100x100 at'					)







def a_     (    )				->					List[Any]:





			"""simple docstring"""







			snake_case:    str      							=canny.gen_gaussian_kernel(9				,							sigma=1.4					)
			# Assert ambiguous array
			assert resp.all()







def a_     (    )				->					Dict:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=imread('digital_image_processing/image_data/lena_small.jpg'				,							0					)
			# assert ambiguous array for all == True
			assert canny_img.all()
			snake_case:    Tuple      							=canny.canny(__UpperCAmelCase					)
			# assert canny array for at least one True
			assert canny_array.any()







def a_     (    )				->					Union[str, Any]:





			"""simple docstring"""







			assert gg.gaussian_filter(__UpperCAmelCase				,							5				,							sigma=0.9					).all()







def a_     (    )				->					Tuple:





			"""simple docstring"""







			snake_case:    Dict      							=array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]					)
			snake_case:    Dict      							=conv.img_convolve(__UpperCAmelCase				,							__UpperCAmelCase					).astype(__UpperCAmelCase					)
			assert res.any()







def a_     (    )				->					Optional[int]:





			"""simple docstring"""







			assert med.median_filter(__UpperCAmelCase				,							3					).any()







def a_     (    )				->					List[Any]:





			"""simple docstring"""







			snake_case							,  snake_case:    Tuple      							=sob.sobel_filter(__UpperCAmelCase					)
			assert grad.any() and theta.any()







def a_     (    )				->					Tuple:





			"""simple docstring"""







			snake_case:    int      							=sp.make_sepia(__UpperCAmelCase				,							20					)
			assert sepia.all()







def a_     (    __UpperCAmelCase = "digital_image_processing/image_data/lena_small.jpg"					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=bs.Burkes(imread(__UpperCAmelCase				,							1					)				,							1_20					)
			burkes.process()
			assert burkes.output_img.any()







def a_     (    __UpperCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,							)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=rs.NearestNeighbour(imread(__UpperCAmelCase				,							1					)				,							4_00				,							2_00					)
			nn.process()
			assert nn.output.any()







def a_     (    )				->					Dict:





			"""simple docstring"""







			snake_case:    Dict      							='digital_image_processing/image_data/lena.jpg'

			# Reading the image and converting it to grayscale.
			snake_case:    Union[str, Any]      							=imread(__UpperCAmelCase				,							0					)

			# Test for get_neighbors_pixel function() return not None
			snake_case:    Union[str, Any]      							=0
			snake_case:    Dict      							=0
			snake_case:    Any      							=image[x_coordinate][y_coordinate]

			snake_case:    int      							=lbp.get_neighbors_pixel(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			snake_case:    str      							=np.zeros((image.shape[0], image.shape[1])					)

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0				,							image.shape[0]					):
						for j in range(0				,							image.shape[1]					):
									snake_case:    Any      							=lbp.local_binary_value(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

			assert lbp_image.any()









'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




