
'''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_	(   _lowercase				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      Dict     =		checkpoints.load_tax_checkpoint(_lowercase				)
					lowerCamelCase_					:      Dict     =		flatten_dict(_lowercase				)
					return flax_params




def lowercase_	(   _lowercase				)     ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_					:      List[str]     =		{}

					lowerCamelCase_					:      str     =		{
					    '''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_					:      Tuple     =		{
					    '''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_					:      Union[str, Any]     =		'''.'''.join(key[1:]				)

															# rename the key
															for old, new in CONVERSION_MAPPING.items():
																				lowerCamelCase_					:      Dict     =		new_key.replace(_lowercase			,       _lowercase				)

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

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

															lowerCamelCase_					:      Optional[int]     =		flax_dict[key]

					lowerCamelCase_					:      str     =		{}
					# 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_	(   _lowercase			,       _lowercase			,       _lowercase=False			,       _lowercase=False				)     ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_					:      str     =		get_flax_param(_lowercase				)

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

					lowerCamelCase_					:      Optional[Any]     =		PixaStructForConditionalGeneration(_lowercase				)

					lowerCamelCase_					:      str     =		rename_and_convert_flax_params(_lowercase				)
					model.load_state_dict(_lowercase				)

					lowerCamelCase_					:      List[str]     =		AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
					lowerCamelCase_					:      List[str]     =		PixaStructImageProcessor()
					lowerCamelCase_					:      int     =		PixaStructProcessor(image_processor=_lowercase			,       tokenizer=_lowercase				)

					if use_large:
										lowerCamelCase_					:      List[Any]     =		4_096

					lowerCamelCase_					:      Tuple     =		True

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

					model.save_pretrained(_lowercase				)
					processor.save_pretrained(_lowercase				)

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


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

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




'''simple docstring'''


def lowercase_	(   _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		0
					while b > 0:
										if b & 1:
															res += a

										a += a
										b >>= 1

					return res




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      str     =		0
					while b > 0:
										if b & 1:
															lowerCamelCase_					:      Optional[int]     =		((res % c) + (a % c)) % c

										a += a
										b >>= 1

					return res


