






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_					)     ->						List[Any]:
					__UpperCamelCase      :					int       		=checkpoints.load_tax_checkpoint(a_					)
					__UpperCamelCase      :					List[str]       		=flatten_dict(a_					)
					return flax_params






def 							A     (       a_					)     ->						Optional[int]:
					__UpperCamelCase      :					Union[str, Any]       		={}

					__UpperCamelCase      :					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',
					}

					__UpperCamelCase      :					Any       		={
					    '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
															__UpperCamelCase      :					Tuple       		='.'.join(key[1:]					)

															# rename the key
															for old, new in CONVERSION_MAPPING.items():
																				__UpperCamelCase      :					Union[str, Any]       		=new_key.replace(a_							,a_					)

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

															if "layers" in new_key and "decoder" not in new_key:
																				# use regex to replace the layer number
																				__UpperCamelCase      :					Dict       		=re.sub(r'layers_(\d+)'							,r'layer.\1'							,a_					)
																				__UpperCamelCase      :					int       		=new_key.replace('encoder'							,'encoder.encoder'					)

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

															__UpperCamelCase      :					Union[str, Any]       		=flax_dict[key]

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

					return converted_torch_dict






def 							A     (       a_							,a_							,a_=False							,a_=False					)     ->						Dict:
					__UpperCamelCase      :					Dict       		=get_flax_param(a_					)

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

					__UpperCamelCase      :					Union[str, Any]       		=PixaStructForConditionalGeneration(a_					)

					__UpperCamelCase      :					Union[str, Any]       		=rename_and_convert_flax_params(a_					)
					model.load_state_dict(a_					)

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

					if use_large:
										__UpperCamelCase      :					int       		=4_096

					__UpperCamelCase      :					Any       		=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__":
							A_       :str       =						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_       :List[Any]       =						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 math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

