import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0			):
    '''simple docstring'''







    if name is None:
        __UpperCamelCase  :Optional[Any]    						=	None
    else:
        __UpperCamelCase  :int    						=	'''.''' * max(0						,    spaces - 2			) + '''# {:''' + str(50 - spaces			) + '''s}'''
        __UpperCamelCase  :Optional[int]    						=	fmt.format(__A			)

    # Print and recurse (if needed).
    if isinstance(__A						,    __A			):
        if msg is not None:
            print(__A			)
        for k in val.keys():
            recursive_print(__A						,    val[k]						,    spaces + 2			)
    elif isinstance(__A						,    torch.Tensor			):
        print(__A						,    ''':'''						,    val.size()			)
    else:
        print(__A						,    ''':'''						,    __A			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        __UpperCamelCase  :int    						=	(num_heads, hidden_size, num_splits) + input_shape[1:]
        __UpperCamelCase  :int    						=	param.view(*__A			)
        __UpperCamelCase  :Optional[Any]    						=	param.transpose(0						,    2			)
        __UpperCamelCase  :Union[str, Any]    						=	param.transpose(1						,    2			).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        __UpperCamelCase  :List[Any]    						=	(num_heads, num_splits, hidden_size) + input_shape[1:]
        __UpperCamelCase  :Tuple    						=	param.view(*__A			)
        __UpperCamelCase  :Any    						=	param.transpose(0						,    1			).contiguous()
    __UpperCamelCase  :str    						=	param.view(*__A			)
    return param



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	{}

    # old versions did not store training args
    __UpperCamelCase  :int    						=	input_state_dict.get('''args'''						,    __A			)
    if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))

        __UpperCamelCase  :Tuple    						=	ds_args.padded_vocab_size
        __UpperCamelCase  :Any    						=	ds_args.max_position_embeddings
        __UpperCamelCase  :Dict    						=	ds_args.hidden_size
        __UpperCamelCase  :Any    						=	ds_args.num_layers
        __UpperCamelCase  :Dict    						=	ds_args.num_attention_heads
        __UpperCamelCase  :int    						=	ds_args.ffn_hidden_size
        # pprint(config)

    # The number of heads.
    __UpperCamelCase  :Any    						=	config.n_head
    # The hidden_size per head.
    __UpperCamelCase  :Optional[int]    						=	config.n_embd // config.n_head
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        __UpperCamelCase  :str    						=	input_state_dict['''checkpoint_version''']
    else:
        __UpperCamelCase  :Any    						=	0.0

    # The model.
    __UpperCamelCase  :Tuple    						=	input_state_dict['''model''']
    # The language model.
    __UpperCamelCase  :Any    						=	model['''language_model''']
    # The embeddings.
    __UpperCamelCase  :Union[str, Any]    						=	lm['''embedding''']

    # The word embeddings.
    __UpperCamelCase  :Optional[Any]    						=	embeddings['''word_embeddings''']['''weight''']
    # Truncate the embedding table to vocab_size rows.
    __UpperCamelCase  :Dict    						=	word_embeddings[: config.vocab_size, :]
    __UpperCamelCase  :str    						=	word_embeddings

    # The position embeddings.
    __UpperCamelCase  :int    						=	embeddings['''position_embeddings''']['''weight''']
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    __UpperCamelCase  :List[str]    						=	pos_embeddings.size(0			)
    if n_positions != config.n_positions:
        raise ValueError(
            f"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match"""			)
    # Store the position embeddings.
    __UpperCamelCase  :List[Any]    						=	pos_embeddings

    # The transformer.
    __UpperCamelCase  :Dict    						=	lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

    # The regex to extract layer names.
    __UpperCamelCase  :Dict    						=	re.compile(R'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''			)

    # The simple map of names for "automated" rules.
    __UpperCamelCase  :List[str]    						=	{
        '''attention.dense''': '''.attn.c_proj.''',
        '''self_attention.dense''': '''.attn.c_proj.''',
        '''mlp.dense_h_to_4h''': '''.mlp.c_fc.''',
        '''mlp.dense_4h_to_h''': '''.mlp.c_proj.''',
    }

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        __UpperCamelCase  :Optional[Any]    						=	layer_re.match(__A			)

        # Stop if that's not a layer
        if m is None:
            break

        # The index of the layer.
        __UpperCamelCase  :List[Any]    						=	int(m.group(1			)			)
        # The name of the operation.
        __UpperCamelCase  :Dict    						=	m.group(2			)
        # Is it a weight or a bias?
        __UpperCamelCase  :Any    						=	m.group(3			)

        # The name of the layer.
        __UpperCamelCase  :int    						=	f"""transformer.h.{layer_idx}"""

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('''layernorm'''			):
            __UpperCamelCase  :List[Any]    						=	'''ln_1''' if op_name.startswith('''input'''			) else '''ln_2'''
            __UpperCamelCase  :Any    						=	val

        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
            # Insert a tensor of 1x1xDxD bias.
            __UpperCamelCase  :List[str]    						=	torch.tril(torch.ones((n_positions, n_positions)						,    dtype=torch.floataa			)			).view(
                1						,    1						,    __A						,    __A			)
            __UpperCamelCase  :Any    						=	causal_mask

            # Insert a "dummy" tensor for masked_bias.
            __UpperCamelCase  :Tuple    						=	torch.tensor(-1e4						,    dtype=torch.floataa			)
            __UpperCamelCase  :int    						=	masked_bias

            __UpperCamelCase  :Optional[Any]    						=	fix_query_key_value_ordering(__A						,    __A						,    3						,    __A						,    __A			)
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            __UpperCamelCase  :Tuple    						=	out_val.transpose(0						,    1			).contiguous()
            # Store.
            __UpperCamelCase  :Any    						=	out_val

        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
            __UpperCamelCase  :List[str]    						=	fix_query_key_value_ordering(__A						,    __A						,    3						,    __A						,    __A			)
            # Store. No change of shape.
            __UpperCamelCase  :Union[str, Any]    						=	out_val

        # Transpose the weights.
        elif weight_or_bias == "weight":
            __UpperCamelCase  :List[str]    						=	megatron_to_transformers[op_name]
            __UpperCamelCase  :Any    						=	val.transpose(0						,    1			)

        # Copy the bias.
        elif weight_or_bias == "bias":
            __UpperCamelCase  :str    						=	megatron_to_transformers[op_name]
            __UpperCamelCase  :str    						=	val

             # DEBUG.
    assert config.n_layer == layer_idx + 1

    # The final layernorm.
    __UpperCamelCase  :List[Any]    						=	transformer['''final_layernorm.weight''']
    __UpperCamelCase  :str    						=	transformer['''final_layernorm.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    __UpperCamelCase  :List[str]    						=	word_embeddings

    # It should be done!
    return output_state_dict



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	argparse.ArgumentParser()
    parser.add_argument('''--print-checkpoint-structure'''						,    action='''store_true'''			)
    parser.add_argument(
        '''path_to_checkpoint'''						,    type=__A						,    help='''Path to the checkpoint file (.zip archive or direct .pt file)'''						,    )
    parser.add_argument(
        '''--config_file'''						,    default=''''''						,    type=__A						,    help='''An optional config json file describing the pre-trained model.'''						,    )
    __UpperCamelCase  :Dict    						=	parser.parse_args()

    # Extract the basename.
    __UpperCamelCase  :Dict    						=	os.path.dirname(args.path_to_checkpoint			)

    # Load the model.
    # the .zip is very optional, let's keep it for backward compatibility
    print(f"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""			)
    if args.path_to_checkpoint.endswith('''.zip'''			):
        with zipfile.ZipFile(args.path_to_checkpoint						,    '''r'''			) as checkpoint:
            with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt'''			) as pytorch_dict:
                __UpperCamelCase  :Optional[Any]    						=	torch.load(__A						,    map_location='''cpu'''			)
    else:
        __UpperCamelCase  :str    						=	torch.load(args.path_to_checkpoint						,    map_location='''cpu'''			)

    __UpperCamelCase  :List[Any]    						=	input_state_dict.get('''args'''						,    __A			)

    # Read the config, or default to the model released by NVIDIA.
    if args.config_file == "":
        if ds_args is not None:
            if ds_args.bias_gelu_fusion:
                __UpperCamelCase  :Optional[Any]    						=	'''gelu_fast'''
            elif ds_args.openai_gelu:
                __UpperCamelCase  :Tuple    						=	'''gelu_new'''
            else:
                __UpperCamelCase  :str    						=	'''gelu'''
        else:
            # in the very early days this used to be "gelu_new"
            __UpperCamelCase  :Dict    						=	'''gelu_new'''

        # Spell out all parameters in case the defaults change.
        __UpperCamelCase  :List[str]    						=	GPTaConfig(
            vocab_size=50_257						,    n_positions=1_024						,    n_embd=1_024						,    n_layer=24						,    n_head=16						,    n_inner=4_096						,    activation_function=__A						,    resid_pdrop=0.1						,    embd_pdrop=0.1						,    attn_pdrop=0.1						,    layer_norm_epsilon=1e-5						,    initializer_range=0.02						,    summary_type='''cls_index'''						,    summary_use_proj=__A						,    summary_activation=__A						,    summary_proj_to_labels=__A						,    summary_first_dropout=0.1						,    scale_attn_weights=__A						,    use_cache=__A						,    bos_token_id=50_256						,    eos_token_id=50_256						,    )
    else:
        __UpperCamelCase  :List[Any]    						=	GPTaConfig.from_json_file(args.config_file			)

    __UpperCamelCase  :Any    						=	['''GPT2LMHeadModel''']

    # Convert.
    print('''Converting'''			)
    __UpperCamelCase  :Dict    						=	convert_megatron_checkpoint(__A						,    __A						,    __A			)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(__A						,    __A			)

    # Add tokenizer class info to config
    # see https://github.com/huggingface/transformers/issues/13906)
    if ds_args is not None:
        __UpperCamelCase  :Tuple    						=	ds_args.tokenizer_type
        if tokenizer_type == "GPT2BPETokenizer":
            __UpperCamelCase  :Optional[int]    						=	'''gpt2'''
        elif tokenizer_type == "PretrainedFromHF":
            __UpperCamelCase  :Any    						=	ds_args.tokenizer_name_or_path
        else:
            raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""			)
    else:
        __UpperCamelCase  :List[Any]    						=	'''gpt2'''

    __UpperCamelCase  :Optional[int]    						=	AutoTokenizer.from_pretrained(__A			)
    __UpperCamelCase  :Any    						=	type(__A			).__name__
    __UpperCamelCase  :Union[str, Any]    						=	tokenizer_class

    # Store the config to file.
    print('''Saving config'''			)
    config.save_pretrained(__A			)

    # Save tokenizer based on args
    print(f"""Adding {tokenizer_class} tokenizer files"""			)
    tokenizer.save_pretrained(__A			)

    # Store the state_dict to file.
    __UpperCamelCase  :Any    						=	os.path.join(__A						,    '''pytorch_model.bin'''			)
    print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
    torch.save(__A						,    __A			)


####################################################################################################

if __name__ == "__main__":
    main()

####################################################################################################






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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="attention"			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    __UpperCamelCase  :Optional[int]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    __UpperCamelCase  :Union[str, Any]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    if split_mlp_wi:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        __UpperCamelCase  :Dict    						=	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        __UpperCamelCase  :Optional[Any]    						=	(wi_a, wi_a)
    else:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
    return wi, wo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    *, SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	traverse_util.flatten_dict(variables['''target''']			)
    __UpperCamelCase  :Optional[int]    						=	{'''/'''.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
    __UpperCamelCase  :Union[str, Any]    						=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''						,    SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict()

    # Shared embeddings.
    __UpperCamelCase  :Any    						=	old['''token_embedder/embedding''']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE			):
        # Block i, layer 0 (Self Attention).
        __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_attention_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''attention'''			)
        __UpperCamelCase  :str    						=	layer_norm
        __UpperCamelCase  :str    						=	k.T
        __UpperCamelCase  :Optional[Any]    						=	o.T
        __UpperCamelCase  :Optional[int]    						=	q.T
        __UpperCamelCase  :Any    						=	v.T

        # Block i, layer 1 (MLP).
        __UpperCamelCase  :Union[str, Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_mlp_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Any    						=	layer_norm
        if split_mlp_wi:
            __UpperCamelCase  :Optional[Any]    						=	wi[0].T
            __UpperCamelCase  :int    						=	wi[1].T
        else:
            __UpperCamelCase  :Dict    						=	wi.T
        __UpperCamelCase  :str    						=	wo.T

    __UpperCamelCase  :Optional[Any]    						=	old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    __UpperCamelCase  :Optional[int]    						=	old['''encoder/encoder_norm/scale''']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE			):
            # Block i, layer 0 (Self Attention).
            __UpperCamelCase  :int    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_self_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''self_attention'''			)
            __UpperCamelCase  :str    						=	layer_norm
            __UpperCamelCase  :Optional[Any]    						=	k.T
            __UpperCamelCase  :List[str]    						=	o.T
            __UpperCamelCase  :Union[str, Any]    						=	q.T
            __UpperCamelCase  :int    						=	v.T

            # Block i, layer 1 (Cross Attention).
            __UpperCamelCase  :Optional[Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_cross_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :int    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''encoder_decoder_attention'''			)
            __UpperCamelCase  :List[str]    						=	layer_norm
            __UpperCamelCase  :Any    						=	k.T
            __UpperCamelCase  :Any    						=	o.T
            __UpperCamelCase  :Optional[int]    						=	q.T
            __UpperCamelCase  :Union[str, Any]    						=	v.T

            # Block i, layer 2 (MLP).
            __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_mlp_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Optional[Any]    						=	layer_norm
            if split_mlp_wi:
                __UpperCamelCase  :int    						=	wi[0].T
                __UpperCamelCase  :Any    						=	wi[1].T
            else:
                __UpperCamelCase  :Optional[Any]    						=	wi.T
            __UpperCamelCase  :Dict    						=	wo.T

        __UpperCamelCase  :Optional[int]    						=	old['''decoder/decoder_norm/scale''']
        __UpperCamelCase  :Optional[Any]    						=	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:
            __UpperCamelCase  :List[str]    						=	old['''decoder/logits_dense/kernel'''].T

    return new



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	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:
        __UpperCamelCase  :Union[str, Any]    						=	state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __UpperCamelCase  :Any    						=	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.'''			)
            __UpperCamelCase  :Optional[int]    						=	state_dict['''shared.weight''']

    return state_dict



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE						,    num_layers=config.num_layers						,    is_encoder_only=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	make_state_dict(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = False			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	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:
        __UpperCamelCase  :Dict    						=	TaEncoderModel(SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase  :str    						=	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
    )





