


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowerCAmelCase	=							logging.get_logger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				b.T
		lowercase__						=				np.sum(np.square(SCREAMING_SNAKE_CASE					)						,    axis=1					)
		lowercase__						=				np.sum(np.square(SCREAMING_SNAKE_CASE					)						,    axis=0					)
		lowercase__						=				np.matmul(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		lowercase__						=				aa[:, None] - 2 * ab + ba[None, :]
		return d
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				x.reshape(-1						,    3					)
		lowercase__						=				squared_euclidean_distance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		return np.argmin(SCREAMING_SNAKE_CASE						,    axis=1					)


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      str  				=			['''pixel_values''']

							def __init__(							self:     int   ,						UpperCamelCase_:     Optional[Union[List[List[int]], np.ndarray]] = None   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     Dict[str, int] = None   ,						UpperCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     bool = True   ,						**UpperCamelCase_:     Dict   ,						)      ->       None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)
									lowercase__						=				size if size is not None else {'''height''': 256, '''width''': 256}
									lowercase__						=				get_size_dict(UpperCamelCase_							)
									lowercase__						=				np.array(UpperCamelCase_							) if clusters is not None else None
									lowercase__						=				do_resize
									lowercase__						=				size
									lowercase__						=				resample
									lowercase__						=				do_normalize
									lowercase__						=				do_color_quantize

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Dict[str, int]   ,						UpperCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase_:     Any   ,						)      ->       np.ndarray:





									"""simple docstring"""







									lowercase__						=				get_size_dict(UpperCamelCase_							)
									if "height" not in size or "width" not in size:
											raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'							)
									return resize(
									    UpperCamelCase_   ,						size=(size['''height'''], size['''width'''])   ,						resample=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						)      ->       np.ndarray:





									"""simple docstring"""







									lowercase__						=				rescale(image=UpperCamelCase_   ,						scale=1 / 127.5   ,						data_format=UpperCamelCase_							)
									lowercase__						=				image - 1
									return image


							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     ImageInput   ,						UpperCamelCase_:     bool = None   ,						UpperCamelCase_:     Dict[str, int] = None   ,						UpperCamelCase_:     PILImageResampling = None   ,						UpperCamelCase_:     bool = None   ,						UpperCamelCase_:     Optional[bool] = None   ,						UpperCamelCase_:     Optional[Union[List[List[int]], np.ndarray]] = None   ,						UpperCamelCase_:     Optional[Union[str, TensorType]] = None   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST   ,						**UpperCamelCase_:     Tuple   ,						)      ->       PIL.Image.Image:





									"""simple docstring"""







									lowercase__						=				do_resize if do_resize is not None else self.do_resize
									lowercase__						=				size if size is not None else self.size
									lowercase__						=				get_size_dict(UpperCamelCase_							)
									lowercase__						=				resample if resample is not None else self.resample
									lowercase__						=				do_normalize if do_normalize is not None else self.do_normalize
									lowercase__						=				do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									lowercase__						=				clusters if clusters is not None else self.clusters
									lowercase__						=				np.array(UpperCamelCase_							)

									lowercase__						=				make_list_of_images(UpperCamelCase_							)

									if not valid_images(UpperCamelCase_							):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

									if do_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

									if do_color_quantize and clusters is None:
											raise ValueError('''Clusters must be specified if do_color_quantize is True.'''							)

									# All transformations expect numpy arrays.
									lowercase__						=				[to_numpy_array(UpperCamelCase_							) for image in images]

									if do_resize:
											lowercase__						=				[self.resize(image=UpperCamelCase_   ,						size=UpperCamelCase_   ,						resample=UpperCamelCase_							) for image in images]

									if do_normalize:
											lowercase__						=				[self.normalize(image=UpperCamelCase_							) for image in images]

									if do_color_quantize:
											lowercase__						=				[to_channel_dimension_format(UpperCamelCase_   ,						ChannelDimension.LAST							) for image in images]
											# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
											lowercase__						=				np.array(UpperCamelCase_							)
											lowercase__						=				color_quantize(UpperCamelCase_   ,						UpperCamelCase_							).reshape(images.shape[:-1]							)

											# flatten to (batch_size, height*width)
											lowercase__						=				images.shape[0]
											lowercase__						=				images.reshape(UpperCamelCase_   ,						-1							)

											# We need to convert back to a list of images to keep consistent behaviour across processors.
											lowercase__						=				list(UpperCamelCase_							)
									else:
											lowercase__						=				[to_channel_dimension_format(UpperCamelCase_   ,						UpperCamelCase_							) for image in images]

									lowercase__						=				{'''input_ids''': images}
									return BatchFeature(data=UpperCamelCase_   ,						tensor_type=UpperCamelCase_							)






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			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)
		lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE					)
		return flax_params
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				{}

		lowercase__						=				{
		    '''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''',
		}

		lowercase__						=				{
		    '''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
						lowercase__						=				'''.'''.join(key[1:]					)

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								lowercase__						=				re.sub(R'''layers_(\d+)'''						,    R'''layer.\1'''						,    SCREAMING_SNAKE_CASE					)
								lowercase__						=				new_key.replace('''encoder'''						,    '''encoder.encoder'''					)

						elif "layers" in new_key and "decoder" in new_key:
								# use regex to replace the layer number
								lowercase__						=				re.sub(R'''layers_(\d+)'''						,    R'''layer.\1'''						,    SCREAMING_SNAKE_CASE					)

						lowercase__						=				flax_dict[key]

		lowercase__						=				{}
		# convert converted_dict into torch format
		for key in converted_dict.keys():
				if ("embed_tokens" not in key) and ("embedder" not in key):
						lowercase__						=				torch.from_numpy(converted_dict[key].T					)
				else:
						lowercase__						=				torch.from_numpy(converted_dict[key]					)

		return converted_torch_dict
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				get_flax_param(SCREAMING_SNAKE_CASE					)

		if not use_large:
				lowercase__						=				PixaStructVisionConfig()
				lowercase__						=				PixaStructTextConfig()
		else:
				lowercase__						=				PixaStructVisionConfig(
				    hidden_size=15_36						,    d_ff=39_68						,    num_attention_heads=24						,    num_hidden_layers=18					)
				lowercase__						=				PixaStructTextConfig(hidden_size=15_36						,    d_ff=39_68						,    num_heads=24						,    num_layers=18					)
		lowercase__						=				PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,    text_config=decoder_config.to_dict()						,    is_vqa=SCREAMING_SNAKE_CASE					)

		lowercase__						=				PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE					)

		lowercase__						=				rename_and_convert_flax_params(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

		lowercase__						=				AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
		lowercase__						=				PixaStructImageProcessor()
		lowercase__						=				PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE						,    tokenizer=SCREAMING_SNAKE_CASE					)

		if use_large:
				lowercase__						=				40_96

		lowercase__						=				True

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

		model.save_pretrained(SCREAMING_SNAKE_CASE					)
		processor.save_pretrained(SCREAMING_SNAKE_CASE					)

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


if __name__ == "__main__":
			lowerCAmelCase	=							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.')
			lowerCAmelCase	=							parser.parse_args()

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


