


'''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      _UpperCamelCase   (		UpperCamelCase__       ):
			UpperCAmelCase__	:       List[str]   =  checkpoints.load_tax_checkpoint(__UpperCAmelCase       )
			UpperCAmelCase__	:       str   =  flatten_dict(__UpperCAmelCase       )
			return flax_params







def      _UpperCamelCase   (		UpperCamelCase__       ):
			UpperCAmelCase__	:       int   =  {}

			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__	:       Union[str, 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__	:       Dict   =  '.'.join(key[1:]       )

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

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															UpperCAmelCase__	:       Optional[Any]   =  new_key.replace(__UpperCAmelCase			,   __UpperCAmelCase       )

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

									UpperCAmelCase__	:       Optional[int]   =  flax_dict[key]

			UpperCAmelCase__	:       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):
									UpperCAmelCase__	:       Union[str, Any]   =  torch.from_numpy(converted_dict[key].T       )
						else:
									UpperCAmelCase__	:       Union[str, Any]   =  torch.from_numpy(converted_dict[key]       )

			return converted_torch_dict







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__=False			,   UpperCamelCase__=False       ):
			UpperCAmelCase__	:       Any   =  get_flax_param(__UpperCAmelCase       )

			if not use_large:
						UpperCAmelCase__	:       Optional[int]   =  PixaStructVisionConfig()
						UpperCAmelCase__	:       int   =  PixaStructTextConfig()
			else:
						UpperCAmelCase__	:       Tuple   =  PixaStructVisionConfig(
						    hidden_size=1_5_3_6			,   d_ff=3_9_6_8			,   num_attention_heads=2_4			,   num_hidden_layers=1_8       )
						UpperCAmelCase__	:       List[Any]   =  PixaStructTextConfig(hidden_size=1_5_3_6			,   d_ff=3_9_6_8			,   num_heads=2_4			,   num_layers=1_8       )
			UpperCAmelCase__	:       Any   =  PixaStructConfig(
			    vision_config=encoder_config.to_dict()			,   text_config=decoder_config.to_dict()			,   is_vqa=__UpperCAmelCase       )

			UpperCAmelCase__	:       List[Any]   =  PixaStructForConditionalGeneration(__UpperCAmelCase       )

			UpperCAmelCase__	:       str   =  rename_and_convert_flax_params(__UpperCAmelCase       )
			model.load_state_dict(__UpperCAmelCase       )

			UpperCAmelCase__	:       List[Any]   =  AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""       )
			UpperCAmelCase__	:       Optional[Any]   =  PixaStructImageProcessor()
			UpperCAmelCase__	:       Optional[int]   =  PixaStructProcessor(image_processor=__UpperCAmelCase			,   tokenizer=__UpperCAmelCase       )

			if use_large:
						UpperCAmelCase__	:       Optional[Any]   =  4_0_9_6

			UpperCAmelCase__	:       str   =  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
		)




'''simple docstring'''

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    list[Stack]      							=[]
			# sort into stacks
			for element in collection:
						snake_case:    int      							=Stack([element]					)
						snake_case:    Union[str, Any]      							=bisect_left(__UpperCAmelCase				,							__UpperCAmelCase					)
						if i != len(__UpperCAmelCase					):
									stacks[i].append(__UpperCAmelCase					)
						else:
									stacks.append(__UpperCAmelCase					)

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


if __name__ == "__main__":
	a							   =				input('Enter numbers separated by a comma:\n').strip()
	a							   =				[int(item) for item in user_input.split(',')]
	print(patience_sort(unsorted))




