




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Optional[Any]:
   snake_case				:    Tuple           =	AutoConfig.from_pretrained(__lowerCamelCase					)
   snake_case				:    str           =	FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase					)
   snake_case				:    int           =	checkpoints.load_tax_checkpoint(__lowerCamelCase					)

   snake_case				:    Optional[Any]           =	"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   if config.model_type == "t5":
      snake_case				:    str           =	"SelfAttention"
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      snake_case				:    Tuple           =	"LocalSelfAttention"
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      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					):
      snake_case				:    str           =	f"""layers_{str(__lowerCamelCase					)}"""

      # Self-Attention
      snake_case				:    Optional[Any]           =	tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      snake_case				:    Union[str, Any]           =	tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      snake_case				:    Optional[Any]           =	tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      snake_case				:    Optional[int]           =	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":
         snake_case				:    List[str]           =	tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

      # Layer Normalization
      snake_case				:    Dict           =	tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

      if split_mlp_wi:
         snake_case				:    Union[str, Any]           =	tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         snake_case				:    Any           =	tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         snake_case				:    str           =	tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      snake_case				:    int           =	tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      snake_case				:    List[Any]           =	tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

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

      snake_case				:    str           =	tax_attention_layer_norm

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

      if split_mlp_wi:
         snake_case				:    List[Any]           =	tax_mlp_wi_a
         snake_case				:    int           =	tax_mlp_wi_a
      else:
         snake_case				:    Any           =	tax_mlp_wi

      snake_case				:    Dict           =	tax_mlp_wo
      snake_case				:    Optional[int]           =	tax_mlp_layer_norm

      snake_case				:    List[Any]           =	flax_model_encoder_layer_block

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

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

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

   # Decoder
   for layer_index in range(config.num_layers					):
      snake_case				:    Optional[Any]           =	f"""layers_{str(__lowerCamelCase					)}"""

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

      # Layer Normalization
      snake_case				:    Union[str, Any]           =	tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

      # Encoder-Decoder-Attention
      snake_case				:    List[str]           =	tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
      snake_case				:    Tuple           =	tax_enc_dec_attention_module["key"]["kernel"]
      snake_case				:    Any           =	tax_enc_dec_attention_module["out"]["kernel"]
      snake_case				:    Tuple           =	tax_enc_dec_attention_module["query"]["kernel"]
      snake_case				:    List[str]           =	tax_enc_dec_attention_module["value"]["kernel"]

      # Layer Normalization
      snake_case				:    List[Any]           =	tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

      snake_case				:    str           =	tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      snake_case				:    Tuple           =	tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

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

      snake_case				:    List[Any]           =	tax_pre_attention_layer_norm

      snake_case				:    Dict           =	tax_enc_dec_attention_key
      snake_case				:    Any           =	tax_enc_dec_attention_out
      snake_case				:    List[Any]           =	tax_enc_dec_attention_query
      snake_case				:    Tuple           =	tax_enc_dec_attention_value

      snake_case				:    List[Any]           =	tax_cross_layer_norm

      if split_mlp_wi:
         snake_case				:    List[Any]           =	tax_mlp_wi_a
         snake_case				:    Any           =	tax_mlp_wi_a
      else:
         snake_case				:    Any           =	tax_mlp_wi

      snake_case				:    Dict           =	tax_mlp_wo

      snake_case				:    Optional[int]           =	txa_mlp_layer_norm

      snake_case				:    List[Any]           =	flax_model_decoder_layer_block

   # Decoder Normalization
   snake_case				:    Tuple           =	tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   snake_case				:    str           =	txa_decoder_norm

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

   # Token Embeddings
   snake_case				:    Optional[Any]           =	tax_model["target"]["token_embedder"]["embedding"]
   snake_case				:    Any           =	txa_token_embeddings

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

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


if __name__ == "__main__":
       lowerCamelCase	:	Tuple				=   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.'
       )
       lowerCamelCase	:	List[str]				=   parser.parse_args()
       convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline




@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE__ (   )  ->       List[Any]:
   with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT					):
      with pytest.raises(lowercase					):
         requests.request("""GET"""						,"""https://huggingface.co"""					)
      with pytest.raises(requests.exceptions.ConnectTimeout					):
         requests.request("""GET"""						,"""https://huggingface.co"""						,timeout=1.0					)




@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE__ (   )  ->       Any:
   with offline(OfflineSimulationMode.CONNECTION_FAILS					):
      with pytest.raises(requests.exceptions.ConnectionError					):
         requests.request("""GET"""						,"""https://huggingface.co"""					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       int:
   with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1					):
      with pytest.raises(lowercase					):
         http_head("""https://huggingface.co"""					)





