
import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				str=0      )					->						str:
				# Format the message.
				if name is None:
								__lowercase								= None
				else:
								__lowercase								= "." * max(0   ,							spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
								__lowercase								= fmt.format(lowercase__      )

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				List[str]   ,							SCREAMING_SNAKE_CASE	:				List[Any]      )					->						str:
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace BERT.
				__lowercase								= param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								__lowercase								= (num_heads, hidden_size, num_splits) + input_shape[1:]
								__lowercase								= param.view(*lowercase__      )
								__lowercase								= param.transpose(0   ,							2      )
								__lowercase								= param.transpose(1   ,							2      ).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								__lowercase								= (num_heads, num_splits, hidden_size) + input_shape[1:]
								__lowercase								= param.view(*lowercase__      )
								__lowercase								= param.transpose(0   ,							1      ).contiguous()
				__lowercase								= param.view(*lowercase__      )
				return param




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				Tuple   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Optional[Any]:
				# The converted output model.
				__lowercase								= {}

				# old versions did not store training args
				__lowercase								= input_state_dict.get('args'   ,							lowercase__      )
				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))

								__lowercase								= ds_args.tokenizer_type
								__lowercase								= ds_args.padded_vocab_size
								__lowercase								= ds_args.max_position_embeddings
								__lowercase								= ds_args.hidden_size
								__lowercase								= ds_args.num_layers
								__lowercase								= ds_args.num_attention_heads
								__lowercase								= ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
								# pprint(config)

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

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

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

				# The position embeddings.
				__lowercase								= 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.
				__lowercase								= pos_embeddings

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

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

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

				# The simple map of names for "automated" rules.
				__lowercase								= {
				    "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.
				__lowercase								= None

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

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

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

								# The name of the layer.
								__lowercase								= F"""bert.encoder.layer.{layer_idx}"""

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith('layernorm'      ):
												__lowercase								= "attention.ln" if op_name.startswith('input'      ) else "ln"
												__lowercase								= 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, ""

												__lowercase								= fix_query_key_value_ordering(lowercase__   ,							lowercase__   ,							3   ,							lowercase__   ,							lowercase__      )
												# Store the tensor as we need the bias as well to interleave QKV and biases.
												__lowercase								= 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.
												__lowercase								= attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												__lowercase								= attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												__lowercase								= attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

												# Clear the stored tensor.
												__lowercase								= None

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

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

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

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

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

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

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

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

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

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

				# It should be done!
				return output_state_dict




def 							__SCREAMING_SNAKE_CASE       (		)					->						Union[str, Any]:
				# Create the argument parser.
				__lowercase								= argparse.ArgumentParser()
				parser.add_argument('--print-checkpoint-structure'   ,							action='store_true'      )
				parser.add_argument('path_to_checkpoint'   ,							type=lowercase__   ,							help='Path to the ZIP file containing the checkpoint'      )
				parser.add_argument(
				    '--config_file'   ,							default=''   ,							type=lowercase__   ,							help='An optional config json file describing the pre-trained model.'   ,							)
				__lowercase								= parser.parse_args()

				# Extract the basename.
				__lowercase								= 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:
																__lowercase								= torch.load(lowercase__   ,							map_location='cpu'      )
				else:
								__lowercase								= torch.load(args.path_to_checkpoint   ,							map_location='cpu'      )

				if args.config_file == "":
								# Default config of megatron-bert 345m
								__lowercase								= 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
								__lowercase								= input_state_dict["model"]["lm_head"]["bias"].numel()
				else:
								__lowercase								= MegatronBertConfig.from_json_file(args.config_file      )

				# Convert.
				print('Converting'      )
				__lowercase								= convert_megatron_checkpoint(lowercase__   ,							lowercase__   ,							lowercase__      )

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

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

				# Store the state_dict to file.
				__lowercase								= os.path.join(lowercase__   ,							'pytorch_model.bin'      )
				print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""      )
				torch.save(lowercase__   ,							lowercase__      )


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

if __name__ == "__main__":
							main()

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



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


											super().__init__(pad_token_id=_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											if self.task == "multiple-choice":
															__lowercase								= {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]    )





