



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       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					checkpoints.load_tax_checkpoint(_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					flatten_dict(_lowerCamelCase						)
							return flax_params






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					{}

							_lowerCAmelCase  :							str				     =					{
							    "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",
							}

							_lowerCAmelCase  :							int				     =					{
							    "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
																					_lowerCAmelCase  :							Optional[int]				     =					".".join(key[1:]						)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												_lowerCAmelCase  :							Any				     =					new_key.replace(_lowerCamelCase       ,				_lowerCamelCase						)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			_lowerCAmelCase  :							List[Any]				     =					new_key.replace(_lowerCamelCase       ,				_lowerCamelCase						)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												_lowerCAmelCase  :							Dict				     =					re.sub(r"layers_(\d+)"       ,				r"layer.\1"       ,				_lowerCamelCase						)
																												_lowerCAmelCase  :							Dict				     =					new_key.replace("encoder"       ,				"encoder.encoder"						)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												_lowerCAmelCase  :							Optional[int]				     =					re.sub(r"layers_(\d+)"       ,				r"layer.\1"       ,				_lowerCamelCase						)

																					_lowerCAmelCase  :							Dict				     =					flax_dict[key]

							_lowerCAmelCase  :							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):
																					_lowerCAmelCase  :							Any				     =					torch.from_numpy(converted_dict[key].T						)
														else:
																					_lowerCAmelCase  :							List[str]				     =					torch.from_numpy(converted_dict[key]						)

							return converted_torch_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=False       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					get_flax_param(_lowerCamelCase						)

							if not use_large:
														_lowerCAmelCase  :							Optional[Any]				     =					PixaStructVisionConfig()
														_lowerCAmelCase  :							Optional[int]				     =					PixaStructTextConfig()
							else:
														_lowerCAmelCase  :							Optional[int]				     =					PixaStructVisionConfig(
														    hidden_size=1_536       ,				d_ff=3_968       ,				num_attention_heads=24       ,				num_hidden_layers=18						)
														_lowerCAmelCase  :							Dict				     =					PixaStructTextConfig(hidden_size=1_536       ,				d_ff=3_968       ,				num_heads=24       ,				num_layers=18						)
							_lowerCAmelCase  :							Union[str, Any]				     =					PixaStructConfig(
							    vision_config=encoder_config.to_dict()       ,				text_config=decoder_config.to_dict()       ,				is_vqa=_lowerCamelCase						)

							_lowerCAmelCase  :							List[str]				     =					PixaStructForConditionalGeneration(_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[int]				     =					rename_and_convert_flax_params(_lowerCamelCase						)
							model.load_state_dict(_lowerCamelCase						)

							_lowerCAmelCase  :							List[str]				     =					AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
							_lowerCAmelCase  :							Tuple				     =					PixaStructImageProcessor()
							_lowerCAmelCase  :							Dict				     =					PixaStructProcessor(image_processor=_lowerCamelCase       ,				tokenizer=_lowerCamelCase						)

							if use_large:
														_lowerCAmelCase  :							Tuple				     =					4_096

							_lowerCAmelCase  :							Union[str, Any]				     =					True

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

							model.save_pretrained(_lowerCamelCase						)
							processor.save_pretrained(_lowerCamelCase						)

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


if __name__ == "__main__":
						_snake_case	=						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	=						parser.parse_args()

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





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'data2vec-vision'







							def __init__(     self,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.0,    __a=0.0,    __a=0.02,    __a=1E-12,    __a=224,    __a=16,    __a=3,    __a=False,    __a=False,    __a=False,    __a=False,    __a=0.1,    __a=0.1,    __a=True,    __a=[3, 5, 7, 11],    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							Dict				     =					hidden_size
														_lowerCAmelCase  :							List[Any]				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					num_attention_heads
														_lowerCAmelCase  :							str				     =					intermediate_size
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_act
														_lowerCAmelCase  :							int				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Dict				     =					initializer_range
														_lowerCAmelCase  :							List[str]				     =					layer_norm_eps

														_lowerCAmelCase  :							Optional[int]				     =					image_size
														_lowerCAmelCase  :							List[Any]				     =					patch_size
														_lowerCAmelCase  :							Optional[Any]				     =					num_channels
														_lowerCAmelCase  :							str				     =					use_mask_token
														_lowerCAmelCase  :							List[str]				     =					use_absolute_position_embeddings
														_lowerCAmelCase  :							str				     =					use_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					use_shared_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					layer_scale_init_value
														_lowerCAmelCase  :							List[Any]				     =					drop_path_rate
														_lowerCAmelCase  :							Union[str, Any]				     =					use_mean_pooling
														# decode head attributes (semantic segmentation)
														_lowerCAmelCase  :							Tuple				     =					out_indices
														_lowerCAmelCase  :							Tuple				     =					pool_scales
														# auxiliary head attributes (semantic segmentation)
														_lowerCAmelCase  :							Optional[int]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							int				     =					auxiliary_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_num_convs
														_lowerCAmelCase  :							int				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					semantic_loss_ignore_index



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4
