



"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''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_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''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_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			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_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    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":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

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

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

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			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_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


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










"""simple docstring"""

import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
     import bitsandbytes as bnb
     import torch
     import torch.nn as nn

     from ..pytorch_utils import ConvaD

if is_accelerate_available():
     from accelerate import init_empty_weights
     from accelerate.utils import find_tied_parameters

lowercase__					:						Optional[int]												=			logging.get_logger(__name__)




def __lowercase    (							_a	,    _a	,    _a	,    _a=None	,    _a=None					):
    # Recurse if needed
    if "." in tensor_name:
        snake_case_				:  Union[str, Any]       =			tensor_name.split('''.'''					)
        for split in splits[:-1]:
            snake_case_				:  Any       =			getattr(_a	,    _a					)
            if new_module is None:
                raise ValueError(f"{module} has no attribute {split}."					)
            snake_case_				:  int       =			new_module
        snake_case_				:  str       =			splits[-1]

    if tensor_name not in module._parameters and tensor_name not in module._buffers:
        raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}."					)
    snake_case_				:  Tuple       =			tensor_name in module._buffers
    snake_case_				:  Optional[int]       =			getattr(_a	,    _a					)

    if old_value.device == torch.device('''meta'''					) and device not in ["meta", torch.device('''meta'''					)] and value is None:
        raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}."					)

    snake_case_				:  Optional[Any]       =			False
    snake_case_				:  List[Any]       =			False
    if is_buffer or not is_bitsandbytes_available():
        snake_case_				:  Optional[Any]       =			False
        snake_case_				:  Tuple       =			False
    else:
        snake_case_				:  Tuple       =			hasattr(bnb.nn	,    '''Params4bit'''					) and isinstance(module._parameters[tensor_name]	,    bnb.nn.Paramsabit					)
        snake_case_				:  int       =			isinstance(module._parameters[tensor_name]	,    bnb.nn.IntaParams					)

    if is_abit or is_abit:
        snake_case_				:  List[str]       =			module._parameters[tensor_name]
        if param.device.type != "cuda":
            if value is None:
                snake_case_				:  Any       =			old_value.to(_a					)
            elif isinstance(_a	,    torch.Tensor					):
                snake_case_				:  str       =			value.to('''cpu'''					)
                if value.dtype == torch.inta:
                    snake_case_				:  List[Any]       =			version.parse(importlib.metadata.version('''bitsandbytes'''					)					) > version.parse(
                        '''0.37.2'''					)
                    if not is_abit_serializable:
                        raise ValueError(
                            '''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
                            '''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'''					)
            else:
                snake_case_				:  Tuple       =			torch.tensor(_a	,    device='''cpu'''					)

            # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
            # Since weights are saved in the correct "orientation", we skip transposing when loading.
            if issubclass(module.source_cls	,    _a					) and fpaa_statistics is None:
                snake_case_				:  Any       =			new_value.T

            snake_case_				:  Tuple       =			old_value.__dict__
            if is_abit:
                snake_case_				:  Tuple       =			bnb.nn.IntaParams(_a	,    requires_grad=_a	,    **_a					).to(_a					)
            elif is_abit:
                snake_case_				:  Any       =			bnb.nn.Paramsabit(_a	,    requires_grad=_a	,    **_a					).to(_a					)

            snake_case_				:  Union[str, Any]       =			new_value
            if fpaa_statistics is not None:
                setattr(module.weight	,    '''SCB'''	,    fpaa_statistics.to(_a					)					)

    else:
        if value is None:
            snake_case_				:  Dict       =			old_value.to(_a					)
        elif isinstance(_a	,    torch.Tensor					):
            snake_case_				:  Dict       =			value.to(_a					)
        else:
            snake_case_				:  str       =			torch.tensor(_a	,    device=_a					)

        if is_buffer:
            snake_case_				:  Optional[int]       =			new_value
        else:
            snake_case_				:  Optional[Any]       =			nn.Parameter(_a	,    requires_grad=old_value.requires_grad					)
            snake_case_				:  List[Any]       =			new_value




def __lowercase    (							_a	,    _a=None	,    _a=None	,    _a=None	,    _a=False					):
    for name, module in model.named_children():
        if current_key_name is None:
            snake_case_				:  List[str]       =			[]
        current_key_name.append(_a					)

        if (isinstance(_a	,    nn.Linear					) or isinstance(_a	,    _a					)) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            if not any(key in '''.'''.join(_a					) for key in modules_to_not_convert					):
                with init_empty_weights():
                    if isinstance(_a	,    _a					):
                        snake_case_,			snake_case_				:  List[Any]       =			module.weight.shape
                    else:
                        snake_case_				:  Dict       =			module.in_features
                        snake_case_				:  Tuple       =			module.out_features

                    if quantization_config.quantization_method() == "llm_int8":
                        snake_case_				:  str       =			bnb.nn.LinearabitLt(
                            _a	,    _a	,    module.bias is not None	,    has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight	,    threshold=quantization_config.llm_inta_threshold	,    )
                        snake_case_				:  str       =			True
                    else:
                        if (
                            quantization_config.llm_inta_skip_modules is not None
                            and name in quantization_config.llm_inta_skip_modules
                        ):
                            pass
                        else:
                            snake_case_				:  Union[str, Any]       =			bnb.nn.Linearabit(
                                _a	,    _a	,    module.bias is not None	,    quantization_config.bnb_abit_compute_dtype	,    compress_statistics=quantization_config.bnb_abit_use_double_quant	,    quant_type=quantization_config.bnb_abit_quant_type	,    )
                            snake_case_				:  List[Any]       =			True
                    # Store the module class in case we need to transpose the weight later
                    snake_case_				:  str       =			type(_a					)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(_a					)
        if len(list(module.children()					)					) > 0:
            snake_case_,			snake_case_				:  Optional[int]       =			_replace_with_bnb_linear(
                _a	,    _a	,    _a	,    _a	,    has_been_replaced=_a	,    )
        # Remove the last key for recursion
        current_key_name.pop(-1					)
    return model, has_been_replaced




def __lowercase    (							_a	,    _a=None	,    _a=None	,    _a=None					):
    snake_case_				:  Any       =			['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
    snake_case_,			snake_case_				:  List[Any]       =			_replace_with_bnb_linear(
        _a	,    _a	,    _a	,    _a					)

    if not has_been_replaced:
        logger.warning(
            '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
            ''' Please double check your model architecture, or submit an issue on github if you think this is'''
            ''' a bug.'''					)

    return model




def __lowercase    (							*_a	,    **_a					):
    warnings.warn(
        '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''	,    _a	,    )
    return replace_with_bnb_linear(*_a	,    **_a					)




def __lowercase    (							*_a	,    **_a					):
    warnings.warn(
        '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''	,    _a	,    )
    return set_module_quantized_tensor_to_device(*_a	,    **_a					)






def __lowercase    (							_a					):
    snake_case_				:  List[str]       =			deepcopy(_a					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
    tied_model.tie_weights()

    snake_case_				:  Optional[Any]       =			find_tied_parameters(_a					)
    # For compatibility with Accelerate < 0.18
    if isinstance(_a	,    _a					):
        snake_case_				:  Any       =			sum(list(tied_params.values()					)	,    []					) + list(tied_params.keys()					)
    else:
        snake_case_				:  str       =			sum(_a	,    []					)
    snake_case_				:  str       =			len(_a					) > 0

    # Check if it is a base model
    snake_case_				:  Dict       =			not hasattr(_a	,    model.base_model_prefix					)

    # Ignore this for base models (BertModel, GPT2Model, etc.)
    if (not has_tied_params) and is_base_model:
        return []

    # otherwise they have an attached head
    snake_case_				:  Dict       =			list(model.named_children()					)
    snake_case_				:  List[Any]       =			[list_modules[-1][0]]

    # add last module together with tied weights
    snake_case_				:  Optional[int]       =			set(_a					) - set(_a					)
    snake_case_				:  List[Any]       =			list(set(_a					)					) + list(_a					)

    # remove ".weight" from the keys
    snake_case_				:  Any       =			['''.weight''', '''.bias''']
    snake_case_				:  List[str]       =			[]
    for name in list_untouched:
        for name_to_remove in names_to_remove:
            if name_to_remove in name:
                snake_case_				:  List[str]       =			name.replace(_a	,    ''''''					)
        filtered_module_names.append(_a					)

    return filtered_module_names





