





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							AutoConfig.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	str						    =							FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							checkpoints.load_tax_checkpoint(__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	List[str]						    =							"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     if config.model_type == "t5":
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							"SelfAttention"
     if config.model_type == "longt5" and config.encoder_attention_type == "local":
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"LocalSelfAttention"
     elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          _SCREAMING_SNAKE_CASE       :	Any						    =							"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	):
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							f"""layers_{str(__lowerCamelCase	)}"""

          # Self-Attention
          _SCREAMING_SNAKE_CASE       :	Any						    =							tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	str						    =							tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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":
               _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

          # Layer Normalization
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               _SCREAMING_SNAKE_CASE       :	Any						    =							tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               _SCREAMING_SNAKE_CASE       :	str						    =							tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							flax_model.params["encoder"]["block"][str(__lowerCamelCase	)]["layer"]
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							tax_attention_key
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_attention_out
          _SCREAMING_SNAKE_CASE       :	str						    =							tax_attention_query
          _SCREAMING_SNAKE_CASE       :	str						    =							tax_attention_value

          _SCREAMING_SNAKE_CASE       :	str						    =							tax_attention_layer_norm

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

          if split_mlp_wi:
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_mlp_wi_a
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_mlp_wi_a
          else:
               _SCREAMING_SNAKE_CASE       :	Tuple						    =							tax_mlp_wi

          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tax_mlp_wo
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_mlp_layer_norm

          _SCREAMING_SNAKE_CASE       :	Dict						    =							flax_model_encoder_layer_block

     # Only for layer 0:
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     _SCREAMING_SNAKE_CASE       :	Any						    =							tax_encoder_rel_embedding

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

     # Assigning
     _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     _SCREAMING_SNAKE_CASE       :	str						    =							tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_layers	):
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							f"""layers_{str(__lowerCamelCase	)}"""

          # Self-Attention
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_enc_dec_attention_module["key"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tax_enc_dec_attention_module["out"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tax_enc_dec_attention_module["query"]["kernel"]
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							tax_enc_dec_attention_module["value"]["kernel"]

          # Layer Normalization
          _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               _SCREAMING_SNAKE_CASE       :	Tuple						    =							tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          _SCREAMING_SNAKE_CASE       :	str						    =							tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							flax_model.params["decoder"]["block"][str(__lowerCamelCase	)]["layer"]
          _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_attention_key
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_attention_out
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_attention_query
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							tax_attention_value

          _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_pre_attention_layer_norm

          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tax_enc_dec_attention_key
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_enc_dec_attention_out
          _SCREAMING_SNAKE_CASE       :	Any						    =							tax_enc_dec_attention_query
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_enc_dec_attention_value

          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tax_cross_layer_norm

          if split_mlp_wi:
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_mlp_wi_a
               _SCREAMING_SNAKE_CASE       :	Any						    =							tax_mlp_wi_a
          else:
               _SCREAMING_SNAKE_CASE       :	int						    =							tax_mlp_wi

          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tax_mlp_wo

          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							txa_mlp_layer_norm

          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							flax_model_decoder_layer_block

     # Decoder Normalization
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     _SCREAMING_SNAKE_CASE       :	Dict						    =							txa_decoder_norm

     # Only for layer 0:
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     _SCREAMING_SNAKE_CASE       :	Dict						    =							tax_decoder_rel_embedding

     # Token Embeddings
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tax_model["target"]["token_embedder"]["embedding"]
     _SCREAMING_SNAKE_CASE       :	Any						    =							txa_token_embeddings

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

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


if __name__ == "__main__":
       UpperCamelCase__      =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.'
       )
       UpperCamelCase__      =parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






from __future__ import annotations

import requests






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
     return requests.get(__lowerCamelCase	).json()






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							requests.get(__lowerCamelCase	).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase	) for story_id in story_ids]






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							hackernews_top_stories(__lowerCamelCase	)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase	) for story in stories	)


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())