

"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase			):







    """simple docstring"""





    @register_to_config
    def __init__(				self			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	int,lowercase_			:	float,lowercase_			:	int,lowercase_			:	int,lowercase_			:	int,lowercase_			:	int,lowercase_			:	str,lowercase_			:	bool = False,)->   List[str]:





        '''simple docstring'''







        super().__init__()

        A__   				=       nn.Embedding(lowercase_,lowercase_		)

        A__   				=       nn.Embedding(lowercase_,lowercase_		)
        A__   				=       False

        A__   				=       nn.Dropout(p=lowercase_		)

        A__   				=       TaConfig(
            vocab_size=lowercase_,d_model=lowercase_,num_heads=lowercase_,d_kv=lowercase_,d_ff=lowercase_,dropout_rate=lowercase_,feed_forward_proj=lowercase_,is_decoder=lowercase_,is_encoder_decoder=lowercase_,)

        A__   				=       nn.ModuleList()
        for lyr_num in range(lowercase_		):
            A__   				=       TaBlock(lowercase_		)
            self.encoders.append(lowercase_		)

        A__   				=       TaLayerNorm(lowercase_		)
        A__   				=       nn.Dropout(p=lowercase_		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	Optional[Any],lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       self.token_embedder(lowercase_		)

        A__   				=       encoder_input_tokens.shape[1]
        A__   				=       torch.arange(lowercase_,device=encoder_input_tokens.device		)
        x += self.position_encoding(lowercase_		)

        A__   				=       self.dropout_pre(lowercase_		)

        # inverted the attention mask
        A__   				=       encoder_input_tokens.size()
        A__   				=       self.get_extended_attention_mask(lowercase_,lowercase_		)

        for lyr in self.encoders:
            A__   				=       lyr(lowercase_,lowercase_		)[0]
        A__   				=       self.layer_norm(lowercase_		)

        return self.dropout_post(lowercase_		), encoder_inputs_mask




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]="attention"				)							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict=False				)							->				str:





    '''simple docstring'''






    if split_mlp_wi:
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
        A__   				=       (wi_a, wi_a)
    else:
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi/kernel']

    A__   				=       params[f'{prefix}/layers_{i}/mlp/wo/kernel']
    return wi, wo
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    return params[f'{prefix}/layers_{i}/{layer_name}/scale']
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     dict   ,      *, SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     bool				)							->				int:





    '''simple docstring'''






    A__   				=       traverse_util.flatten_dict(variables['target']				)
    A__   				=       {'/'.join(SCREAMING_SNAKE_CASE__				): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    A__   				=       'encoder/layers_0/mlp/wi_0/kernel' in old
    print('Split MLP:'   ,      SCREAMING_SNAKE_CASE__				)

    A__   				=       collections.OrderedDict()

    # Shared embeddings.
    A__   				=       old['token_embedder/embedding']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE__				):
        # Block i, layer 0 (Self Attention).
        A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'pre_attention_layer_norm'				)
        A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'attention'				)
        A__   				=       layer_norm
        A__   				=       k.T
        A__   				=       o.T
        A__   				=       q.T
        A__   				=       v.T

        # Block i, layer 1 (MLP).
        A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'pre_mlp_layer_norm'				)
        A__				,						A__   				=       tax_mlp_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       layer_norm
        if split_mlp_wi:
            A__   				=       wi[0].T
            A__   				=       wi[1].T
        else:
            A__   				=       wi.T
        A__   				=       wo.T

    A__   				=       old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    A__   				=       old['encoder/encoder_norm/scale']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE__				):
            # Block i, layer 0 (Self Attention).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_self_attention_layer_norm'				)
            A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'self_attention'				)
            A__   				=       layer_norm
            A__   				=       k.T
            A__   				=       o.T
            A__   				=       q.T
            A__   				=       v.T

            # Block i, layer 1 (Cross Attention).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_cross_attention_layer_norm'				)
            A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'encoder_decoder_attention'				)
            A__   				=       layer_norm
            A__   				=       k.T
            A__   				=       o.T
            A__   				=       q.T
            A__   				=       v.T

            # Block i, layer 2 (MLP).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_mlp_layer_norm'				)
            A__				,						A__   				=       tax_mlp_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       layer_norm
            if split_mlp_wi:
                A__   				=       wi[0].T
                A__   				=       wi[1].T
            else:
                A__   				=       wi.T
            A__   				=       wo.T

        A__   				=       old['decoder/decoder_norm/scale']
        A__   				=       old[
            'decoder/relpos_bias/rel_embedding'
        ].T

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            A__   				=       old['decoder/logits_dense/kernel'].T

    return new
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     bool				)							->				Dict:





    '''simple docstring'''






    A__   				=       collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        A__   				=       state_dict['shared.weight']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            A__   				=       state_dict['shared.weight']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('Using shared word embeddings as lm_head.'				)
            A__   				=       state_dict['shared.weight']

    return state_dict
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple				)							->				int:





    '''simple docstring'''






    A__   				=       checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)
    A__   				=       convert_tax_to_pytorch(SCREAMING_SNAKE_CASE__   ,      num_layers=config.num_layers   ,      is_encoder_only=SCREAMING_SNAKE_CASE__				)
    A__   				=       make_state_dict(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    model.load_state_dict(SCREAMING_SNAKE_CASE__   ,      strict=SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     bool = False				)							->				Any:





    '''simple docstring'''






    A__   				=       TaConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
    print(f'Building PyTorch model from configuration: {config}'				)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        A__   				=       TaEncoderModel(SCREAMING_SNAKE_CASE__				)
    else:
        A__   				=       TaForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(SCREAMING_SNAKE_CASE__				)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE__				)
    print('Done'				)


if __name__ == "__main__":
     lowercase_			=  argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
     # Required parameters
     parser.add_argument(
         "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
     )
     lowercase_			=  parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )

