
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[Any]:







				"""simple docstring"""



				__A   			= AutoConfig.from_pretrained(a_						)
				__A   			= FlaxAutoModelForSeqaSeqLM.from_config(config=a_						)
				__A   			= checkpoints.load_tax_checkpoint(a_						)

				__A   			= "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

				if config.model_type == "t5":
								__A   			= "SelfAttention"
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								__A   			= "LocalSelfAttention"
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__A   			= "TransientGlobalSelfAttention"
				else:
								raise ValueError(
								    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
								    " attribute with a value from ['local', 'transient-global]."						)

				# Encoder
				for layer_index in range(config.num_layers						):
								__A   			= F'''layers_{str(a_						)}'''

								# Self-Attention
								__A   			= tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
								__A   			= tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
								__A   			= tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
								__A   			= tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__A   			= tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

								# Layer Normalization
								__A   			= tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

								if split_mlp_wi:
												__A   			= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
												__A   			= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
								else:
												__A   			= tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

								__A   			= tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

								# Layer Normalization
								__A   			= tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

								# Assigning
								__A   			= flax_model.params["encoder"]["block"][str(a_						)]["layer"]
								__A   			= tax_attention_key
								__A   			= tax_attention_out
								__A   			= tax_attention_query
								__A   			= tax_attention_value

								__A   			= tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__A   			= tax_global_layer_norm

								if split_mlp_wi:
												__A   			= tax_mlp_wi_a
												__A   			= tax_mlp_wi_a
								else:
												__A   			= tax_mlp_wi

								__A   			= tax_mlp_wo
								__A   			= tax_mlp_layer_norm

								__A   			= flax_model_encoder_layer_block

				# Only for layer 0:
				__A   			= tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
				__A   			= tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__A   			= tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
								__A   			= tax_encoder_global_rel_embedding

				# Assigning
				__A   			= tax_model["target"]["encoder"]["encoder_norm"]["scale"]
				__A   			= tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers						):
								__A   			= F'''layers_{str(a_						)}'''

								# Self-Attention
								__A   			= tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
								__A   			= tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
								__A   			= tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
								__A   			= tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

								# Layer Normalization
								__A   			= tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
								    "scale"
								]

								# Encoder-Decoder-Attention
								__A   			= tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
								__A   			= tax_enc_dec_attention_module["key"]["kernel"]
								__A   			= tax_enc_dec_attention_module["out"]["kernel"]
								__A   			= tax_enc_dec_attention_module["query"]["kernel"]
								__A   			= tax_enc_dec_attention_module["value"]["kernel"]

								# Layer Normalization
								__A   			= tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

								# MLP
								if split_mlp_wi:
												__A   			= tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
												__A   			= tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
								else:
												__A   			= tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

								__A   			= tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

								# Layer Normalization
								__A   			= tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

								# Assigning
								__A   			= flax_model.params["decoder"]["block"][str(a_						)]["layer"]
								__A   			= tax_attention_key
								__A   			= tax_attention_out
								__A   			= tax_attention_query
								__A   			= tax_attention_value

								__A   			= tax_pre_attention_layer_norm

								__A   			= tax_enc_dec_attention_key
								__A   			= tax_enc_dec_attention_out
								__A   			= tax_enc_dec_attention_query
								__A   			= tax_enc_dec_attention_value

								__A   			= tax_cross_layer_norm

								if split_mlp_wi:
												__A   			= tax_mlp_wi_a
												__A   			= tax_mlp_wi_a
								else:
												__A   			= tax_mlp_wi

								__A   			= tax_mlp_wo

								__A   			= txa_mlp_layer_norm

								__A   			= flax_model_decoder_layer_block

				# Decoder Normalization
				__A   			= tax_model["target"]["decoder"]["decoder_norm"]["scale"]
				__A   			= txa_decoder_norm

				# Only for layer 0:
				__A   			= tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
				__A   			= tax_decoder_rel_embedding

				# Token Embeddings
				__A   			= tax_model["target"]["token_embedder"]["embedding"]
				__A   			= txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								__A   			= tax_model["target"]["decoder"]["logits_dense"]["kernel"]

				flax_model.save_pretrained(a_						)
				print("T5X Model was sucessfully converted!"						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
					)
					parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
					parser.add_argument(
					    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
					)
					SCREAMING_SNAKE_CASE							:Tuple							 =				parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
