


'''simple docstring'''






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      UpperCAmelCase      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :str="attention" ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
		snake_case_		:							Any			       =						params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
		snake_case_		:							Any			       =						params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
		snake_case_		:							Optional[int]			       =						params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
		return k, o, q, v





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :Dict=False ):







		'''simple docstring'''




		if split_mlp_wi:
				snake_case_		:							Optional[Any]			       =						params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
				snake_case_		:							Dict			       =						params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
				snake_case_		:							Union[str, Any]			       =						(wi_a, wi_a)
		else:
				snake_case_		:							Tuple			       =						params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

		snake_case_		:							Tuple			       =						params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
		return wi, wo





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      UpperCAmelCase      (		lowerCamelCase_  :dict				,      *, lowerCamelCase_  :int				,      lowerCamelCase_  :bool ):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						traverse_util.flatten_dict(variables["""target"""] )
		snake_case_		:							Dict			       =						{"/".join(_lowerCAmelCase ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		snake_case_		:							Tuple			       =						"encoder/layers_0/mlp/wi_0/kernel" in old
		print("""Split MLP:"""				,      _lowerCAmelCase )

		snake_case_		:							Optional[int]			       =						collections.OrderedDict()

		# Shared embeddings.
		snake_case_		:							Optional[int]			       =						old["token_embedder/embedding"]

		# Encoder.
		for i in range(_lowerCAmelCase ):
				# Block i, layer 0 (Self Attention).
				snake_case_		:							int			       =						tax_layer_norm_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """encoder"""				,      """pre_attention_layer_norm""" )
				snake_case_		:							Optional[Any]			       =						tax_attention_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """encoder"""				,      """attention""" )
				snake_case_		:							List[Any]			       =						layer_norm
				snake_case_		:							List[str]			       =						k.T
				snake_case_		:							Tuple			       =						o.T
				snake_case_		:							str			       =						q.T
				snake_case_		:							Dict			       =						v.T

				# Block i, layer 1 (MLP).
				snake_case_		:							str			       =						tax_layer_norm_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """encoder"""				,      """pre_mlp_layer_norm""" )
				snake_case_		:							Union[str, Any]			       =						tax_mlp_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """encoder"""				,      _lowerCAmelCase )
				snake_case_		:							int			       =						layer_norm
				if split_mlp_wi:
						snake_case_		:							Dict			       =						wi[0].T
						snake_case_		:							List[Any]			       =						wi[1].T
				else:
						snake_case_		:							Optional[Any]			       =						wi.T
				snake_case_		:							Optional[int]			       =						wo.T

		snake_case_		:							Optional[Any]			       =						old[
		    "encoder/relpos_bias/rel_embedding"
		].T
		snake_case_		:							Optional[Any]			       =						old["encoder/encoder_norm/scale"]

		if not is_encoder_only:
				# Decoder.
				for i in range(_lowerCAmelCase ):
						# Block i, layer 0 (Self Attention).
						snake_case_		:							Optional[int]			       =						tax_layer_norm_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """decoder"""				,      """pre_self_attention_layer_norm""" )
						snake_case_		:							List[str]			       =						tax_attention_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """decoder"""				,      """self_attention""" )
						snake_case_		:							Optional[Any]			       =						layer_norm
						snake_case_		:							Any			       =						k.T
						snake_case_		:							Any			       =						o.T
						snake_case_		:							Optional[int]			       =						q.T
						snake_case_		:							List[str]			       =						v.T

						# Block i, layer 1 (Cross Attention).
						snake_case_		:							Any			       =						tax_layer_norm_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """decoder"""				,      """pre_cross_attention_layer_norm""" )
						snake_case_		:							int			       =						tax_attention_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """decoder"""				,      """encoder_decoder_attention""" )
						snake_case_		:							int			       =						layer_norm
						snake_case_		:							Union[str, Any]			       =						k.T
						snake_case_		:							List[str]			       =						o.T
						snake_case_		:							Any			       =						q.T
						snake_case_		:							str			       =						v.T

						# Block i, layer 2 (MLP).
						snake_case_		:							Optional[int]			       =						tax_layer_norm_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """decoder"""				,      """pre_mlp_layer_norm""" )
						snake_case_		:							Optional[int]			       =						tax_mlp_lookup(_lowerCAmelCase				,      _lowerCAmelCase				,      """decoder"""				,      _lowerCAmelCase )
						snake_case_		:							Union[str, Any]			       =						layer_norm
						if split_mlp_wi:
								snake_case_		:							Optional[int]			       =						wi[0].T
								snake_case_		:							List[Any]			       =						wi[1].T
						else:
								snake_case_		:							Dict			       =						wi.T
						snake_case_		:							Dict			       =						wo.T

				snake_case_		:							Union[str, Any]			       =						old["decoder/decoder_norm/scale"]
				snake_case_		:							Union[str, Any]			       =						old[
				    "decoder/relpos_bias/rel_embedding"
				].T

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						snake_case_		:							Tuple			       =						old["decoder/logits_dense/kernel"].T

		return new





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :bool ):







		'''simple docstring'''
		snake_case_		:							Any			       =						collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				snake_case_		:							Any			       =						state_dict["shared.weight"]

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						snake_case_		:							Tuple			       =						state_dict["shared.weight"]

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print("""Using shared word embeddings as lm_head.""" )
						snake_case_		:							Optional[Any]			       =						state_dict["shared.weight"]

		return state_dict





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :int				,      lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		snake_case_		:							int			       =						checkpoints.load_tax_checkpoint(_lowerCAmelCase )
		snake_case_		:							Tuple			       =						convert_tax_to_pytorch(_lowerCAmelCase				,      num_layers=config.num_layers				,      is_encoder_only=_lowerCAmelCase )
		snake_case_		:							List[Any]			       =						make_state_dict(_lowerCAmelCase				,      _lowerCAmelCase )
		model.load_state_dict(_lowerCAmelCase				,      strict=_lowerCAmelCase )





def      UpperCAmelCase      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :int				,      lowerCamelCase_  :bool = False ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						TaConfig.from_json_file(_lowerCAmelCase )
		print(F'''Building PyTorch model from configuration: {config}''' )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				snake_case_		:							Optional[Any]			       =						TaEncoderModel(_lowerCAmelCase )
		else:
				snake_case_		:							Any			       =						TaForConditionalGeneration(_lowerCAmelCase )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(_lowerCAmelCase				,      _lowerCAmelCase				,      _lowerCAmelCase				,      _lowerCAmelCase )

		# Save pytorch-model
		print(F'''Save PyTorch model to {pytorch_dump_path}''' )
		model.save_pretrained(_lowerCAmelCase )

		# Verify that we can load the checkpoint.
		model.from_pretrained(_lowerCAmelCase )
		print("""Done""" )


if __name__ == "__main__":
		__A	:	Union[str, Any]     =     argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
		# Required parameters
		parser.add_argument(
		    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
		)
		__A	:	Optional[int]     =     parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		# Return True if there is node that has not iterated.
		snake_case_		:							Optional[int]			       =						[False] * len(lowerCamelCase_ )
		snake_case_		:							Dict			       =						[]
		queue.append(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						True

		while queue:
				snake_case_		:							Union[str, Any]			       =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(lowerCamelCase_ )
								snake_case_		:							str			       =						True
								snake_case_		:							Optional[Any]			       =						u

		return visited[t]





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		# This array is filled by BFS and to store path
		snake_case_		:							Optional[int]			       =						[-1] * (len(lowerCamelCase_ ))
		snake_case_		:							Tuple			       =						0
		while bfs(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							int			       =						float("""Inf""" )
				snake_case_		:							Any			       =						sink

				while s != source:
						# Find the minimum value in select path
						snake_case_		:							Optional[Any]			       =						min(lowerCamelCase_				,      graph[parent[s]][s] )
						snake_case_		:							Union[str, Any]			       =						parent[s]

				max_flow += path_flow
				snake_case_		:							Dict			       =						sink

				while v != source:
						snake_case_		:							Optional[int]			       =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						snake_case_		:							Any			       =						parent[v]
		return max_flow


__A	:	Union[str, Any]     =     [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__A,   __A	:	Union[str, Any]     =     0, 5






print(ford_fulkerson(graph, source, sink))