





'''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  (			lowerCamelCase_     :					Tuple			):
			__lowercase			  =							checkpoints.load_tax_checkpoint(lowerCamelCase_			)
			__lowercase			  =							flatten_dict(lowerCamelCase_			)
			return flax_params




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			__lowercase			  =							{}

			__lowercase			  =							{
			    '''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''',
			}

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

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

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

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

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

									__lowercase			  =							flax_dict[key]

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

			return converted_torch_dict




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Dict						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[str]=False						,    lowerCamelCase_     :					str=False			):
			__lowercase			  =							get_flax_param(lowerCamelCase_			)

			if not use_large:
						__lowercase			  =							PixaStructVisionConfig()
						__lowercase			  =							PixaStructTextConfig()
			else:
						__lowercase			  =							PixaStructVisionConfig(
						    hidden_size=1_5_3_6						,    d_ff=3_9_6_8						,    num_attention_heads=2_4						,    num_hidden_layers=1_8			)
						__lowercase			  =							PixaStructTextConfig(hidden_size=1_5_3_6						,    d_ff=3_9_6_8						,    num_heads=2_4						,    num_layers=1_8			)
			__lowercase			  =							PixaStructConfig(
			    vision_config=encoder_config.to_dict()						,    text_config=decoder_config.to_dict()						,    is_vqa=lowerCamelCase_			)

			__lowercase			  =							PixaStructForConditionalGeneration(lowerCamelCase_			)

			__lowercase			  =							rename_and_convert_flax_params(lowerCamelCase_			)
			model.load_state_dict(lowerCamelCase_			)

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

			if use_large:
						__lowercase			  =							4_0_9_6

			__lowercase			  =							True

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

			model.save_pretrained(lowerCamelCase_			)
			processor.save_pretrained(lowerCamelCase_			)

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


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE   =      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.''')
	_SCREAMING_SNAKE_CASE   =      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 heapq




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict			):
			__lowercase			  =							[]

			# for each node and his adjacency list add them and the rank of the node to queue
			# using heapq module the queue will be filled like a Priority Queue
			# heapq works with a min priority queue, so I used -1*len(v) to build it
			for key, value in graph.items():
						# O(log(n))
						heapq.heappush(lowerCamelCase_						,    [-1 * len(lowerCamelCase_			), (key, value)]			)

			# chosen_vertices = set of chosen vertices
			__lowercase			  =							set()

			# while queue isn't empty and there are still edges
			#   (queue[0][0] is the rank of the node with max rank)
			while queue and queue[0][0] != 0:
						# extract vertex with max rank from queue and add it to chosen_vertices
						__lowercase			  =							heapq.heappop(lowerCamelCase_			)[1][0]
						chosen_vertices.add(lowerCamelCase_			)

						# Remove all arcs adjacent to argmax
						for elem in queue:
									# if v haven't adjacent node, skip
									if elem[0] == 0:
												continue
									# if argmax is reachable from elem
									# remove argmax from elem's adjacent list and update his rank
									if argmax in elem[1][1]:
												__lowercase			  =							elem[1][1].index(lowerCamelCase_			)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(lowerCamelCase_			)
			return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_SCREAMING_SNAKE_CASE   =      {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')





