


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_				(      UpperCAmelCase__  ):
					lowercase_  							=			checkpoints.load_tax_checkpoint(UpperCAmelCase__  )
					lowercase_  							=			flatten_dict(UpperCAmelCase__  )
					return flax_params


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					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(UpperCAmelCase__						,							UpperCAmelCase__  )

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

															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"""						,							UpperCAmelCase__  )
																				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"""						,							UpperCAmelCase__  )

															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_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=False						,							UpperCAmelCase__=False  ):
					lowercase_  							=			get_flax_param(UpperCAmelCase__  )

					if not use_large:
										lowercase_  							=			PixaStructVisionConfig()
										lowercase_  							=			PixaStructTextConfig()
					else:
										lowercase_  							=			PixaStructVisionConfig(
										    hidden_size=1_5_3_6						,							d_ff=3_9_6_8						,							num_attention_heads=2_4						,							num_hidden_layers=1_8  )
										lowercase_  							=			PixaStructTextConfig(hidden_size=1_5_3_6						,							d_ff=3_9_6_8						,							num_heads=2_4						,							num_layers=1_8  )
					lowercase_  							=			PixaStructConfig(
					    vision_config=encoder_config.to_dict()						,							text_config=decoder_config.to_dict()						,							is_vqa=UpperCAmelCase__  )

					lowercase_  							=			PixaStructForConditionalGeneration(UpperCAmelCase__  )

					lowercase_  							=			rename_and_convert_flax_params(UpperCAmelCase__  )
					model.load_state_dict(UpperCAmelCase__  )

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

					if use_large:
										lowercase_  							=			4_0_9_6

					lowercase_  							=			True

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

					model.save_pretrained(UpperCAmelCase__  )
					processor.save_pretrained(UpperCAmelCase__  )

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


if __name__ == "__main__":
							a					  =     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.')
							a					  =     parser.parse_args()

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






def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										raise TypeError("""Input value must be an 'int' type"""  )

					lowercase_  							=			0
					while number:
										position += 1
										number >>= 1

					return position


if __name__ == "__main__":
							import doctest

							doctest.testmod()


