



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 	snake_case__       (					__lowercase       )		->		str:







						"""simple docstring"""

						A__	:							str						    = checkpoints.load_tax_checkpoint(__lowercase       )
						A__	:							Optional[int]						    = flatten_dict(__lowercase       )
						return flax_params
def 	snake_case__       (					__lowercase       )		->		Optional[Any]:







						"""simple docstring"""

						A__	:							int						    = {}

						A__	:							Union[str, 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",
						}

						A__	:							str						    = {
						    "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
																		A__	:							List[str]						    = ".".join(key[1:]       )

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

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														A__	:							str						    = new_key.replace(__lowercase							,     __lowercase       )

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								A__	:							Union[str, Any]						    = re.sub(r"layers_(\d+)"							,     r"layer.\1"							,     __lowercase       )
																								A__	:							int						    = new_key.replace("encoder"							,     "encoder.encoder"       )

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								A__	:							Dict						    = re.sub(r"layers_(\d+)"							,     r"layer.\1"							,     __lowercase       )

																		A__	:							List[Any]						    = flax_dict[key]

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

						return converted_torch_dict
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase=False							,     __lowercase=False       )		->		List[Any]:







						"""simple docstring"""

						A__	:							Optional[Any]						    = get_flax_param(__lowercase       )

						if not use_large:
												A__	:							List[Any]						    = PixaStructVisionConfig()
												A__	:							Optional[Any]						    = PixaStructTextConfig()
						else:
												A__	:							Optional[Any]						    = PixaStructVisionConfig(
												    hidden_size=1_5_3_6							,     d_ff=3_9_6_8							,     num_attention_heads=2_4							,     num_hidden_layers=1_8       )
												A__	:							Optional[Any]						    = PixaStructTextConfig(hidden_size=1_5_3_6							,     d_ff=3_9_6_8							,     num_heads=2_4							,     num_layers=1_8       )
						A__	:							Optional[int]						    = PixaStructConfig(
						    vision_config=encoder_config.to_dict()							,     text_config=decoder_config.to_dict()							,     is_vqa=__lowercase       )

						A__	:							List[str]						    = PixaStructForConditionalGeneration(__lowercase       )

						A__	:							List[Any]						    = rename_and_convert_flax_params(__lowercase       )
						model.load_state_dict(__lowercase       )

						A__	:							Dict						    = AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"       )
						A__	:							List[str]						    = PixaStructImageProcessor()
						A__	:							int						    = PixaStructProcessor(image_processor=__lowercase							,     tokenizer=__lowercase       )

						if use_large:
												A__	:							Optional[Any]						    = 4_0_9_6

						A__	:							Optional[int]						    = True

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

						model.save_pretrained(__lowercase       )
						processor.save_pretrained(__lowercase       )

						print("Model saved in {}".format(__lowercase       )       )


if __name__ == "__main__":
		snake_case    :						Any  =						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.')
		snake_case    :						Optional[int]  =						parser.parse_args()

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




from collections.abc import Callable

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		np.array:







						"""simple docstring"""

						A__	:							Any						    = int(np.ceil((x_end - xa) / step_size       )       )
						A__	:							Union[str, Any]						    = np.zeros((n + 1,)       )
						A__	:							Any						    = ya
						A__	:							Union[str, Any]						    = xa

						for k in range(__lowercase       ):
												A__	:							Any						    = y[k] + step_size * ode_func(__lowercase							,     y[k]       )
												A__	:							Any						    = y[k] + (
												    (step_size / 2) * (ode_func(__lowercase							,     y[k]       ) + ode_func(x + step_size							,     __lowercase       ))
												)
												x += step_size

						return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()