
'''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      A (       A_							:					int       ):
						snake_case       :     str =    checkpoints.load_tax_checkpoint(A_       )
						snake_case       :     Optional[Any] =    flatten_dict(A_       )
						return flax_params

def      A (       A_							:					Optional[int]       ):
						snake_case       :     Tuple =    {}

						snake_case       :     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''',
						}

						snake_case       :     Optional[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
																		snake_case       :     Optional[Any] =    '''.'''.join(key[1:]       )

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								snake_case       :     int =    new_key.replace(A_    ,							A_       )

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														snake_case       :     Tuple =    new_key.replace(A_    ,							A_       )

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								snake_case       :     Optional[int] =    re.sub(R'''layers_(\d+)'''    ,							R'''layer.\1'''    ,							A_       )
																								snake_case       :     str =    new_key.replace('''encoder'''    ,							'''encoder.encoder'''       )

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

																		snake_case       :     Optional[int] =    flax_dict[key]

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

						return converted_torch_dict

def      A (       A_							:					Dict    ,							A_							:					List[Any]    ,							A_							:					int=False    ,							A_							:					List[Any]=False       ):
						snake_case       :     List[Any] =    get_flax_param(A_       )

						if not use_large:
												snake_case       :     List[Any] =    PixaStructVisionConfig()
												snake_case       :     List[str] =    PixaStructTextConfig()
						else:
												snake_case       :     Optional[int] =    PixaStructVisionConfig(
												    hidden_size=1536    ,							d_ff=3968    ,							num_attention_heads=24    ,							num_hidden_layers=18       )
												snake_case       :     List[str] =    PixaStructTextConfig(hidden_size=1536    ,							d_ff=3968    ,							num_heads=24    ,							num_layers=18       )
						snake_case       :     str =    PixaStructConfig(
						    vision_config=encoder_config.to_dict()    ,							text_config=decoder_config.to_dict()    ,							is_vqa=A_       )

						snake_case       :     str =    PixaStructForConditionalGeneration(A_       )

						snake_case       :     List[Any] =    rename_and_convert_flax_params(A_       )
						model.load_state_dict(A_       )

						snake_case       :     Union[str, Any] =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''       )
						snake_case       :     int =    PixaStructImageProcessor()
						snake_case       :     List[Any] =    PixaStructProcessor(image_processor=A_    ,							tokenizer=A_       )

						if use_large:
												snake_case       :     str =    4096

						snake_case       :     Optional[int] =    True

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

						model.save_pretrained(A_       )
						processor.save_pretrained(A_       )

						print('''Model saved in {}'''.format(A_       )       )


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

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



'''simple docstring'''





from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip

