





'''simple docstring'''

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Dict						,	_lowerCamelCase : Union[str, Any]	):
    A__    =      AutoConfig.from_pretrained(__lowerCamelCase	)
    A__    =      FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase	)
    A__    =      checkpoints.load_tax_checkpoint(__lowerCamelCase	)

    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(__lowerCamelCase	)}"

        # 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(__lowerCamelCase	)]["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(__lowerCamelCase	)}"

        # 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(__lowerCamelCase	)]["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(__lowerCamelCase	)
    print("T5X Model was sucessfully converted!"	)


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










'''simple docstring'''

def 						UpperCamelCase    (							):
    A__    =      [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

    A__    =      6
    A__    =      1
    A__    =      19_01

    A__    =      0

    while year < 20_01:
        day += 7

        if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
            if day > days_per_month[month - 1] and month != 2:
                month += 1
                A__    =      day - days_per_month[month - 2]
            elif day > 29 and month == 2:
                month += 1
                A__    =      day - 29
        else:
            if day > days_per_month[month - 1]:
                month += 1
                A__    =      day - days_per_month[month - 2]

        if month > 12:
            year += 1
            A__    =      1

        if year < 20_01 and day == 1:
            sundays += 1
    return sundays


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



