





'''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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = checkpoints.load_tax_checkpoint(lowerCAmelCase_							)
				lowercase         = flatten_dict(lowerCAmelCase_							)
				return flax_params







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""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(lowerCAmelCase_		,							lowerCAmelCase_							)

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

												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"		,							lowerCAmelCase_							)
																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"		,							lowerCAmelCase_							)

												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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=False		,							lowerCAmelCase_=False							):

				"""simple docstring"""






				lowercase         = get_flax_param(lowerCAmelCase_							)

				if not use_large:
								lowercase         = PixaStructVisionConfig()
								lowercase         = PixaStructTextConfig()
				else:
								lowercase         = PixaStructVisionConfig(
								    hidden_size=1536		,							d_ff=3968		,							num_attention_heads=24		,							num_hidden_layers=18							)
								lowercase         = PixaStructTextConfig(hidden_size=1536		,							d_ff=3968		,							num_heads=24		,							num_layers=18							)
				lowercase         = PixaStructConfig(
				    vision_config=encoder_config.to_dict()		,							text_config=decoder_config.to_dict()		,							is_vqa=lowerCAmelCase_							)

				lowercase         = PixaStructForConditionalGeneration(lowerCAmelCase_							)

				lowercase         = rename_and_convert_flax_params(lowerCAmelCase_							)
				model.load_state_dict(lowerCAmelCase_							)

				lowercase         = AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"							)
				lowercase         = PixaStructImageProcessor()
				lowercase         = PixaStructProcessor(image_processor=lowerCAmelCase_		,							tokenizer=lowerCAmelCase_							)

				if use_large:
								lowercase         = 4096

				lowercase         = 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__":
			__lowerCamelCase : Dict											=			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 : Optional[Any]											=			parser.parse_args()

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











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=_lowercase  )
class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		field(default='''image-classification'''			,       metadata={'''include_in_asdict_even_if_is_default''': True}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''image''': Image()}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''labels''': ClassLabel}  )
		UpperCAmelCase					:	str 			=		"image"
		UpperCAmelCase					:	str 			=		"labels"




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Union[str, Any]				)					-> Union[str, Any]:
						if self.label_column not in features:
										raise ValueError(f'Column {self.label_column} is not present in features.'				)
						if not isinstance(features[self.label_column]							,			A__				):
										raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
						lowercase         = copy.deepcopy(self				)
						lowercase         = self.label_schema.copy()
						lowercase         = features[self.label_column]
						lowercase         = label_schema
						return task_template





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict[str, str]:
						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}




