



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
		)




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

snake_case    :						Optional[int]  =						get_logger()

snake_case    :						Optional[dict]  =						None




class 							lowerCAmelCase__  (	TensorFormatter[Mapping, 'jax.Array', Mapping]						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Optional[int]=None			,		_A      :     str=None			,		**_A      :     Tuple):
											super().__init__(features=_A)
											import jax
											from jaxlib.xla_client import Device

											if isinstance(_A			,		_A):
																	raise ValueError(
																	    F'Expected {device} to be a `str` not {type(_A)}, as `jaxlib.xla_extension.Device` '
																	    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
																	    "the device with `str()` to get its string identifier that will be internally mapped "
																	    "to the actual `jaxlib.xla_extension.Device`.")
											A__	:							Union[str, Any]						    = device if isinstance(_A			,		_A) else str(jax.devices()[0])
											# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
											# with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Any						    = self._map_devices_to_str()
											if self.device not in list(DEVICE_MAPPING.keys()):
																	logger.warning(
																	    F'Device with string identifier {self.device} not listed among the available '
																	    F'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
																	    F'device: {str(jax.devices()[0])}.')
																	A__	:							str						    = str(jax.devices()[0])
											A__	:							List[Any]						    = jnp_array_kwargs







					@staticmethod
					def  _lowercase			(       ):
											import jax

											return {str(_A): device for device in jax.devices()}







					def  _lowercase			(       self      :     Tuple			,		_A      :     int):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		_A) and column:
																	if all(
																	    isinstance(_A			,		jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
																							return jnp.stack(_A			,		axis=0)
											return column







					def  _lowercase			(       self      :     List[str]			,		_A      :     Union[str, Any]):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		(str, bytes, type(_A))):
																	return value
											elif isinstance(_A			,		(np.character, np.ndarray)) and np.issubdtype(value.dtype			,		np.character):
																	return value.tolist()

											A__	:							Dict						    = {}

											if isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.integer):
																	# the default int precision depends on the jax config
																	# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																	if jax.config.jax_enable_xaa:
																							A__	:							Union[str, Any]						    = {"dtype": jnp.intaa}
																	else:
																							A__	:							int						    = {"dtype": jnp.intaa}
											elif isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.floating):
																	A__	:							str						    = {"dtype": jnp.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(_A			,		PIL.Image.Image):
																							A__	:							Any						    = np.asarray(_A)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Optional[Any]						    = self._map_devices_to_str()

											with jax.default_device(DEVICE_MAPPING[self.device]):
																	# calling jnp.array on a np.ndarray does copy the data
																	# see https://github.com/google/jax/issues/4486
																	return jnp.array(_A			,		**{**default_dtype, **self.jnp_array_kwargs})







					def  _lowercase			(       self      :     Dict			,		_A      :     Any):
											import jax

											# support for torch, tf, jax etc.
											if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(_A			,		torch.Tensor):
																							return self._tensorize(data_struct.detach().cpu().numpy()[()])
											if hasattr(_A			,		"__array__") and not isinstance(_A			,		jax.Array):
																	A__	:							Any						    = data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_A			,		np.ndarray):
																	if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																							return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											elif isinstance(_A			,		(list, tuple)):
																	return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											return self._tensorize(_A)







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     dict):
											return map_nested(self._recursive_tensorize			,		_A			,		map_list=_A)







					def  _lowercase			(       self      :     List[str]			,		_A      :     pa.Table):
											A__	:							Dict						    = self.numpy_arrow_extractor().extract_row(_A)
											A__	:							int						    = self.python_features_decoder.decode_row(_A)
											return self.recursive_tensorize(_A)







					def  _lowercase			(       self      :     int			,		_A      :     pa.Table):
											A__	:							int						    = self.numpy_arrow_extractor().extract_column(_A)
											A__	:							Optional[int]						    = self.python_features_decoder.decode_column(_A			,		pa_table.column_names[0])
											A__	:							Dict						    = self.recursive_tensorize(_A)
											A__	:							List[Any]						    = self._consolidate(_A)
											return column







					def  _lowercase			(       self      :     Tuple			,		_A      :     pa.Table):
											A__	:							Any						    = self.numpy_arrow_extractor().extract_batch(_A)
											A__	:							str						    = self.python_features_decoder.decode_batch(_A)
											A__	:							Optional[Any]						    = self.recursive_tensorize(_A)
											for column_name in batch:
																	A__	:							Optional[int]						    = self._consolidate(batch[column_name])
											return batch