


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Optional[int]:



       '''simple docstring'''





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

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

       if config.model_type == "t5":
              UpperCAmelCase__										=     "SelfAttention"
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              UpperCAmelCase__										=     "LocalSelfAttention"
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              UpperCAmelCase__										=     "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			):
              UpperCAmelCase__										=     F"""layers_{str(__A			)}"""

              # Self-Attention
              UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
              UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
              UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
              UpperCAmelCase__										=     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":
                     UpperCAmelCase__										=     tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

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

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

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

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

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

              UpperCAmelCase__										=     tax_attention_layer_norm

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

              if split_mlp_wi:
                     UpperCAmelCase__										=     tax_mlp_wi_a
                     UpperCAmelCase__										=     tax_mlp_wi_a
              else:
                     UpperCAmelCase__										=     tax_mlp_wi

              UpperCAmelCase__										=     tax_mlp_wo
              UpperCAmelCase__										=     tax_mlp_layer_norm

              UpperCAmelCase__										=     flax_model_encoder_layer_block

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

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

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

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

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

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

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

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

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

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

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

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

              UpperCAmelCase__										=     tax_pre_attention_layer_norm

              UpperCAmelCase__										=     tax_enc_dec_attention_key
              UpperCAmelCase__										=     tax_enc_dec_attention_out
              UpperCAmelCase__										=     tax_enc_dec_attention_query
              UpperCAmelCase__										=     tax_enc_dec_attention_value

              UpperCAmelCase__										=     tax_cross_layer_norm

              if split_mlp_wi:
                     UpperCAmelCase__										=     tax_mlp_wi_a
                     UpperCAmelCase__										=     tax_mlp_wi_a
              else:
                     UpperCAmelCase__										=     tax_mlp_wi

              UpperCAmelCase__										=     tax_mlp_wo

              UpperCAmelCase__										=     txa_mlp_layer_norm

              UpperCAmelCase__										=     flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
    A      = 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."
    )
    A      = 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 math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





