

'''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 		__lowerCamelCase			(  __snake_case	:						List[Any]			)			->				Optional[Any]:






						"""simple docstring"""





						A__	:  Any           =checkpoints.load_tax_checkpoint(snake_case__			)
						A__	:  Optional[int]           =flatten_dict(snake_case__			)
						return flax_params
def 		__lowerCamelCase			(  __snake_case	:						Tuple			)			->				Tuple:






						"""simple docstring"""





						A__	:  Any           ={}

						A__	:  List[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""",
						}

						A__	:  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
																		A__	:  int           =""".""".join(key[1:]			)

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

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														A__	:  Union[str, Any]           =new_key.replace(snake_case__,						snake_case__			)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								A__	:  Union[str, Any]           =re.sub(r"""layers_(\d+)""",						r"""layer.\1""",						snake_case__			)
																								A__	:  Any           =new_key.replace("""encoder""",						"""encoder.encoder"""			)

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

																		A__	:  Any           =flax_dict[key]

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

						return converted_torch_dict
def 		__lowerCamelCase			(  __snake_case	:						str,						__snake_case	:						List[str],						__snake_case	:						Union[str, Any]=False,						__snake_case	:						Union[str, Any]=False			)			->				Union[str, Any]:






						"""simple docstring"""





						A__	:  int           =get_flax_param(snake_case__			)

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

						A__	:  List[str]           =PixaStructForConditionalGeneration(snake_case__			)

						A__	:  List[Any]           =rename_and_convert_flax_params(snake_case__			)
						model.load_state_dict(snake_case__			)

						A__	:  Tuple           =AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""			)
						A__	:  int           =PixaStructImageProcessor()
						A__	:  int           =PixaStructProcessor(image_processor=snake_case__,						tokenizer=snake_case__			)

						if use_large:
												A__	:  Any           =4_096

						A__	:  Optional[Any]           =True

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

						model.save_pretrained(snake_case__			)
						processor.save_pretrained(snake_case__			)

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


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					:      Dict 		=     parser.parse_args()

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






from bisect import bisect
from itertools import accumulate







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  sorted(zip(snake_case__    ,			snake_case__					)    ,			key=lambda snake_case__					: x[0] / x[1]    ,			reverse=snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__		  =  list(accumulate(snake_case__					)					)
				lowerCAmelCase__		  =  bisect(snake_case__    ,			snake_case__					)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k]					)
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
