

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=0 )->	List[Any]:



    """simple docstring"""






    if name is None:
        UpperCamelCase_      =	None
    else:
        UpperCamelCase_      =	"." * max(0 ,	spaces - 2 ) + "# {:" + str(5_0 - spaces ) + "s}"
        UpperCamelCase_      =	fmt.format(UpperCamelCase__ )

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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Dict:



    """simple docstring"""






    UpperCamelCase_      =	param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        UpperCamelCase_      =	(num_heads, hidden_size, num_splits) + input_shape[1:]
        UpperCamelCase_      =	param.view(*UpperCamelCase__ )
        UpperCamelCase_      =	param.transpose(0 ,	2 )
        UpperCamelCase_      =	param.transpose(1 ,	2 ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        UpperCamelCase_      =	(num_heads, num_splits, hidden_size) + input_shape[1:]
        UpperCamelCase_      =	param.view(*UpperCamelCase__ )
        UpperCamelCase_      =	param.transpose(0 ,	1 ).contiguous()
    UpperCamelCase_      =	param.view(*UpperCamelCase__ )
    return param




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Tuple:



    """simple docstring"""






    UpperCamelCase_      =	{}

    # old versions did not store training args
    UpperCamelCase_      =	input_state_dict.get("args" ,	UpperCamelCase__ )
    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_      =	ds_args.tokenizer_type
        UpperCamelCase_      =	ds_args.padded_vocab_size
        UpperCamelCase_      =	ds_args.max_position_embeddings
        UpperCamelCase_      =	ds_args.hidden_size
        UpperCamelCase_      =	ds_args.num_layers
        UpperCamelCase_      =	ds_args.num_attention_heads
        UpperCamelCase_      =	ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
        # pprint(config)

    # The number of heads.
    UpperCamelCase_      =	config.num_attention_heads
    # The hidden_size per head.
    UpperCamelCase_      =	config.hidden_size // heads
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        UpperCamelCase_      =	input_state_dict["checkpoint_version"]
    else:
        UpperCamelCase_      =	0.0

    # The model.
    UpperCamelCase_      =	input_state_dict["model"]
    # The language model.
    UpperCamelCase_      =	model["language_model"]
    # The embeddings.
    UpperCamelCase_      =	lm["embedding"]

    # The word embeddings.
    UpperCamelCase_      =	embeddings["word_embeddings"]["weight"]
    # Truncate the embedding table to vocab_size rows.
    UpperCamelCase_      =	word_embeddings[: config.vocab_size, :]
    # Store the word embeddings.
    UpperCamelCase_      =	word_embeddings

    # The position embeddings.
    UpperCamelCase_      =	embeddings["position_embeddings"]["weight"]
    assert pos_embeddings.size(0 ) == config.max_position_embeddings and pos_embeddings.size(1 ) == config.hidden_size
    # Store the position embeddings.
    UpperCamelCase_      =	pos_embeddings

    # The token-type embeddings.
    UpperCamelCase_      =	embeddings["tokentype_embeddings"]["weight"]
    # Store the position embeddings.
    UpperCamelCase_      =	tokentype_embeddings

    # The transformer.
    UpperCamelCase_      =	lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

    # The regex to extract layer names.
    UpperCamelCase_      =	re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)" )

    # The simple map of names for "automated" rules.
    UpperCamelCase_      =	{
        "attention.dense": ".attention.output.dense.",
        "self_attention.dense": ".attention.output.dense.",
        "mlp.dense_h_to_4h": ".intermediate.dense.",
        "mlp.dense_4h_to_h": ".output.dense.",
    }

    # Keep track of the attention/query/value tensor.
    UpperCamelCase_      =	None

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        UpperCamelCase_      =	layer_re.match(UpperCamelCase__ )

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

        # The index of the layer.
        UpperCamelCase_      =	int(m.group(1 ) )
        # The name of the operation.
        UpperCamelCase_      =	m.group(2 )
        # Is it a weight or a bias?
        UpperCamelCase_      =	m.group(3 )

        # The name of the layer.
        UpperCamelCase_      =	f"bert.encoder.layer.{layer_idx}"

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm" ):
            UpperCamelCase_      =	"attention.ln" if op_name.startswith("input" ) else "ln"
            UpperCamelCase_      =	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":
            # Make sure the QKV pointer is nil.
            assert attention_qkv_weight is None, ""

            UpperCamelCase_      =	fix_query_key_value_ordering(UpperCamelCase__ ,	UpperCamelCase__ ,	3 ,	UpperCamelCase__ ,	UpperCamelCase__ )
            # Store the tensor as we need the bias as well to interleave QKV and biases.
            UpperCamelCase_      =	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":
            # Make sure we read the weight tensor.
            assert attention_qkv_weight is not None, ""

            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
            UpperCamelCase_      =	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            UpperCamelCase_      =	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            UpperCamelCase_      =	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

            UpperCamelCase_      =	fix_query_key_value_ordering(UpperCamelCase__ ,	UpperCamelCase__ ,	3 ,	UpperCamelCase__ ,	UpperCamelCase__ )
            # Split the bias.
            UpperCamelCase_      =	out_val[0 * config.hidden_size : 1 * config.hidden_size]
            UpperCamelCase_      =	out_val[1 * config.hidden_size : 2 * config.hidden_size]
            UpperCamelCase_      =	out_val[2 * config.hidden_size : 3 * config.hidden_size]

            # Store.
            UpperCamelCase_      =	q
            UpperCamelCase_      =	q_bias
            UpperCamelCase_      =	k
            UpperCamelCase_      =	k_bias
            UpperCamelCase_      =	v
            UpperCamelCase_      =	v_bias

            # Clear the stored tensor.
            UpperCamelCase_      =	None

        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
            UpperCamelCase_      =	megatron_to_transformers[op_name]
            UpperCamelCase_      =	val

    # The final layernorm.
    UpperCamelCase_      =	transformer["final_layernorm.weight"]
    UpperCamelCase_      =	transformer["final_layernorm.bias"]

    # The pooler.
    UpperCamelCase_      =	lm["pooler"]

    # Store the matrix and the bias.
    UpperCamelCase_      =	pooler["dense.weight"]
    UpperCamelCase_      =	pooler["dense.bias"]

    # The LM head from Megatron (for RACE).
    UpperCamelCase_      =	model["lm_head"]

    # The transform matrix.
    UpperCamelCase_      =	lm_head["dense.weight"]
    UpperCamelCase_      =	lm_head["dense.bias"]

    # The transform LN.
    UpperCamelCase_      =	lm_head["layernorm.weight"]
    UpperCamelCase_      =	lm_head["layernorm.bias"]

    # For the decoder, we replicate the weights.
    UpperCamelCase_      =	word_embeddings
    UpperCamelCase_      =	lm_head["bias"]

    # The classifier from Megatron (for MLNI).
    UpperCamelCase_      =	model["binary_head"]

    # Store the classifier.
    UpperCamelCase_      =	binary_head["weight"]
    UpperCamelCase_      =	binary_head["bias"]

    # It should be done!
    return output_state_dict




def  lowerCAmelCase(			)->	Optional[int]:



    """simple docstring"""






    UpperCamelCase_      =	argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure" ,	action="store_true" )
    parser.add_argument("path_to_checkpoint" ,	type=UpperCamelCase__ ,	help="Path to the ZIP file containing the checkpoint" )
    parser.add_argument(
        "--config_file" ,	default="" ,	type=UpperCamelCase__ ,	help="An optional config json file describing the pre-trained model." ,	)
    UpperCamelCase_      =	parser.parse_args()

    # Extract the basename.
    UpperCamelCase_      =	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_      =	torch.load(UpperCamelCase__ ,	map_location="cpu" )
    else:
        UpperCamelCase_      =	torch.load(args.path_to_checkpoint ,	map_location="cpu" )

    if args.config_file == "":
        # Default config of megatron-bert 345m
        UpperCamelCase_      =	MegatronBertConfig()

        # different megatron-bert-*-345m models have different vocab sizes, so override the default
        # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
        UpperCamelCase_      =	input_state_dict["model"]["lm_head"]["bias"].numel()
    else:
        UpperCamelCase_      =	MegatronBertConfig.from_json_file(args.config_file )

    # Convert.
    print("Converting" )
    UpperCamelCase_      =	convert_megatron_checkpoint(UpperCamelCase__ ,	UpperCamelCase__ ,	UpperCamelCase__ )

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

    # Store the config to file.
    print("Saving config" )
    config.save_pretrained(UpperCamelCase__ )

    # Store the state_dict to file.
    UpperCamelCase_      =	os.path.join(UpperCamelCase__ ,	"pytorch_model.bin" )
    print(f"Saving checkpoint to \"{output_checkpoint_file}\"" )
    torch.save(UpperCamelCase__ ,	UpperCamelCase__ )


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

if __name__ == "__main__":
 main()

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[Any]				  =    [
     """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """T5EncoderModel""",
     """T5ForConditionalGeneration""",
     """T5Model""",
     """T5PreTrainedModel""",
     """load_tf_weights_in_t5""",
     """T5ForQuestionAnswering""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """FlaxT5EncoderModel""",
     """FlaxT5ForConditionalGeneration""",
     """FlaxT5Model""",
     """FlaxT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
