




'''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 	snake_case_					(UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							_a			  =       checkpoints.load_tax_checkpoint(UpperCamelCase   )
							_a			  =       flatten_dict(UpperCamelCase   )
							return flax_params
def 	snake_case_					(UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       {}

							_a			  =       {
							    '''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''',
							}

							_a			  =       {
							    '''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
																					_a			  =       '''.'''.join(key[1:]   )

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												_a			  =       new_key.replace(UpperCamelCase				,		UpperCamelCase   )

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

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												_a			  =       re.sub(R'''layers_(\d+)'''				,		R'''layer.\1'''				,		UpperCamelCase   )
																												_a			  =       new_key.replace('''encoder'''				,		'''encoder.encoder'''   )

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												_a			  =       re.sub(R'''layers_(\d+)'''				,		R'''layer.\1'''				,		UpperCamelCase   )

																					_a			  =       flax_dict[key]

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

							return converted_torch_dict
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Any=False				,		UpperCamelCase :						Optional[Any]=False   ):





							'''simple docstring'''


							_a			  =       get_flax_param(UpperCamelCase   )

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

							_a			  =       PixaStructForConditionalGeneration(UpperCamelCase   )

							_a			  =       rename_and_convert_flax_params(UpperCamelCase   )
							model.load_state_dict(UpperCamelCase   )

							_a			  =       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''   )
							_a			  =       PixaStructImageProcessor()
							_a			  =       PixaStructProcessor(image_processor=UpperCamelCase				,		tokenizer=UpperCamelCase   )

							if use_large:
														_a			  =       4096

							_a			  =       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__":
				_snake_case   :   List[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   :   Tuple     							=	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 os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
				import ctypes
				import msvcrt  # noqa

				class     A					(	ctypes.Structure					):
											# _fields is a specific attr expected by ctypes
											lowercase_           =       [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def 	snake_case_					():





							'''simple docstring'''


							if os.name == "nt":
														_a			  =       CursorInfo()
														_a			  =       ctypes.windll.kernelaa.GetStdHandle(-11   )
														ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
														_a			  =       False
														ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
							elif os.name == "posix":
														sys.stdout.write('''\033[?25l'''   )
														sys.stdout.flush()
def 	snake_case_					():





							'''simple docstring'''


							if os.name == "nt":
														_a			  =       CursorInfo()
														_a			  =       ctypes.windll.kernelaa.GetStdHandle(-11   )
														ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
														_a			  =       True
														ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
							elif os.name == "posix":
														sys.stdout.write('''\033[?25h'''   )
														sys.stdout.flush()

@contextmanager
def 	snake_case_					():





							'''simple docstring'''


							try:
														hide_cursor()
														yield
							finally:
														show_cursor()

