



"""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      _lowercase							(    __snake_case      )		->  List[str]:
			__lowerCAmelCase :    Optional[Any]				=			checkpoints.load_tax_checkpoint(__snake_case      )
			__lowerCAmelCase :    List[Any]				=			flatten_dict(__snake_case      )
			return flax_params


def      _lowercase							(    __snake_case      )		->  Tuple:
			__lowerCAmelCase :    str				=			{}

			__lowerCAmelCase :    List[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",
			}

			__lowerCAmelCase :    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
									__lowerCAmelCase :    int				=			".".join(key[1:]      )

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												__lowerCAmelCase :    List[Any]				=			new_key.replace(__snake_case   ,__snake_case      )

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															__lowerCAmelCase :    int				=			new_key.replace(__snake_case   ,__snake_case      )

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

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												__lowerCAmelCase :    List[Any]				=			re.sub(r"layers_(\d+)"   ,r"layer.\1"   ,__snake_case      )

									__lowerCAmelCase :    Dict				=			flax_dict[key]

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

			return converted_torch_dict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=False   ,__snake_case=False      )		->  str:
			__lowerCAmelCase :    Optional[Any]				=			get_flax_param(__snake_case      )

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

			__lowerCAmelCase :    Union[str, Any]				=			PixaStructForConditionalGeneration(__snake_case      )

			__lowerCAmelCase :    List[Any]				=			rename_and_convert_flax_params(__snake_case      )
			model.load_state_dict(__snake_case      )

			__lowerCAmelCase :    Optional[Any]				=			AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"      )
			__lowerCAmelCase :    Dict				=			PixaStructImageProcessor()
			__lowerCAmelCase :    Union[str, Any]				=			PixaStructProcessor(image_processor=__snake_case   ,tokenizer=__snake_case      )

			if use_large:
						__lowerCAmelCase :    Optional[Any]				=			4_096

			__lowerCAmelCase :    Optional[Any]				=			True

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

			model.save_pretrained(__snake_case      )
			processor.save_pretrained(__snake_case      )

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


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				:  List[str] =       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 argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)