






import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig

def   lowerCAmelCase__						(     _a							:      Optional[int]	,   _a							:      Any	,   _a							:      Any=0     ):




		if name is None:
				snake_case_					:				List[Any]    			=							None
		else:
				snake_case_					:				Union[str, Any]    			=							"." * max(0	,   spaces - 2     ) + "# {:" + str(50 - spaces     ) + "s}"
				snake_case_					:				Optional[Any]    			=							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__						(     _a							:      List[str]	,   _a							:      List[str]	,   _a							:      Union[str, Any]	,   _a							:      List[str]	,   _a							:      List[str]     ):
		snake_case_					:				Optional[Any]    			=							param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				snake_case_					:				str    			=							(num_heads, hidden_size, num_splits) + input_shape[1:]
				snake_case_					:				str    			=							param.view(*_UpperCamelCase     )
				snake_case_					:				Dict    			=							param.transpose(0	,   2     )
				snake_case_					:				List[Any]    			=							param.transpose(1	,   2     ).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				snake_case_					:				Optional[int]    			=							(num_heads, num_splits, hidden_size) + input_shape[1:]
				snake_case_					:				Optional[Any]    			=							param.view(*_UpperCamelCase     )
				snake_case_					:				List[str]    			=							param.transpose(0	,   1     ).contiguous()
		snake_case_					:				List[Any]    			=							param.view(*_UpperCamelCase     )
		return param

def   lowerCAmelCase__						(     _a							:      int	,   _a							:      int	,   _a							:      Optional[int]     ):
		snake_case_					:				Optional[int]    			=							{}

		# old versions did not store training args
		snake_case_					:				Any    			=							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))

				snake_case_					:				Any    			=							ds_args.padded_vocab_size
				snake_case_					:				Optional[Any]    			=							ds_args.max_position_embeddings
				snake_case_					:				Tuple    			=							ds_args.hidden_size
				snake_case_					:				Union[str, Any]    			=							ds_args.num_layers
				snake_case_					:				Any    			=							ds_args.num_attention_heads
				snake_case_					:				str    			=							ds_args.ffn_hidden_size
				# pprint(config)

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

		# The model.
		snake_case_					:				int    			=							input_state_dict["model"]
		# The language model.
		snake_case_					:				str    			=							model["language_model"]
		# The embeddings.
		snake_case_					:				Optional[Any]    			=							lm["embedding"]

		# The word embeddings.
		snake_case_					:				Optional[Any]    			=							embeddings["word_embeddings"]["weight"]
		# Truncate the embedding table to vocab_size rows.
		snake_case_					:				List[Any]    			=							word_embeddings[: config.vocab_size, :]
		snake_case_					:				Optional[int]    			=							word_embeddings

		# The position embeddings.
		snake_case_					:				Optional[int]    			=							embeddings["position_embeddings"]["weight"]
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		snake_case_					:				List[Any]    			=							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.
		snake_case_					:				Optional[int]    			=							pos_embeddings

		# The transformer.
		snake_case_					:				List[str]    			=							lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

		# The regex to extract layer names.
		snake_case_					:				Optional[int]    			=							re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"     )

		# The simple map of names for "automated" rules.
		snake_case_					:				Union[str, Any]    			=							{
		    "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.
				snake_case_					:				Any    			=							layer_re.match(_UpperCamelCase     )

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

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

				# The name of the layer.
				snake_case_					:				List[str]    			=							F'''transformer.h.{layer_idx}'''

				# For layernorm(s), simply store the layer norm.
				if op_name.endswith("layernorm"     ):
						snake_case_					:				Optional[Any]    			=							"ln_1" if op_name.startswith("input"     ) else "ln_2"
						snake_case_					:				str    			=							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.
						snake_case_					:				Any    			=							torch.tril(torch.ones((n_positions, n_positions)	,   dtype=torch.floataa     )     ).view(
						    1	,   1	,   _UpperCamelCase	,   _UpperCamelCase     )
						snake_case_					:				List[str]    			=							causal_mask

						# Insert a "dummy" tensor for masked_bias.
						snake_case_					:				int    			=							torch.tensor(-1E4	,   dtype=torch.floataa     )
						snake_case_					:				Optional[Any]    			=							masked_bias

						snake_case_					:				Union[str, Any]    			=							fix_query_key_value_ordering(_UpperCamelCase	,   _UpperCamelCase	,   3	,   _UpperCamelCase	,   _UpperCamelCase     )
						# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
						snake_case_					:				int    			=							out_val.transpose(0	,   1     ).contiguous()
						# Store.
						snake_case_					:				Tuple    			=							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":
						snake_case_					:				int    			=							fix_query_key_value_ordering(_UpperCamelCase	,   _UpperCamelCase	,   3	,   _UpperCamelCase	,   _UpperCamelCase     )
						# Store. No change of shape.
						snake_case_					:				Tuple    			=							out_val

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

				# Copy the bias.
				elif weight_or_bias == "bias":
						snake_case_					:				int    			=							megatron_to_transformers[op_name]
						snake_case_					:				Dict    			=							val

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

		# The final layernorm.
		snake_case_					:				Optional[Any]    			=							transformer["final_layernorm.weight"]
		snake_case_					:				Optional[int]    			=							transformer["final_layernorm.bias"]

		# For LM head, transformers' wants the matrix to weight embeddings.
		snake_case_					:				Optional[Any]    			=							word_embeddings

		# It should be done!
		return output_state_dict

def   lowerCAmelCase__						(     ):
		snake_case_					:				Union[str, Any]    			=							argparse.ArgumentParser()
		parser.add_argument("--print-checkpoint-structure"	,   action="store_true"     )
		parser.add_argument(
		    "path_to_checkpoint"	,   type=_UpperCamelCase	,   help="Path to the checkpoint file (.zip archive or direct .pt file)"	,   )
		parser.add_argument(
		    "--config_file"	,   default=""	,   type=_UpperCamelCase	,   help="An optional config json file describing the pre-trained model."	,   )
		snake_case_					:				Tuple    			=							parser.parse_args()

		# Extract the basename.
		snake_case_					:				Optional[int]    			=							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:
								snake_case_					:				int    			=							torch.load(_UpperCamelCase	,   map_location="cpu"     )
		else:
				snake_case_					:				Union[str, Any]    			=							torch.load(args.path_to_checkpoint	,   map_location="cpu"     )

		snake_case_					:				List[Any]    			=							input_state_dict.get("args"	,   _UpperCamelCase     )

		# 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:
								snake_case_					:				List[str]    			=							"gelu_fast"
						elif ds_args.openai_gelu:
								snake_case_					:				Optional[int]    			=							"gelu_new"
						else:
								snake_case_					:				Tuple    			=							"gelu"
				else:
						# in the very early days this used to be "gelu_new"
						snake_case_					:				Union[str, Any]    			=							"gelu_new"

				# Spell out all parameters in case the defaults change.
				snake_case_					:				Dict    			=							GPTaConfig(
				    vocab_size=5_02_57	,   n_positions=10_24	,   n_embd=10_24	,   n_layer=24	,   n_head=16	,   n_inner=40_96	,   activation_function=_UpperCamelCase	,   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=_UpperCamelCase	,   summary_activation=_UpperCamelCase	,   summary_proj_to_labels=_UpperCamelCase	,   summary_first_dropout=0.1	,   scale_attn_weights=_UpperCamelCase	,   use_cache=_UpperCamelCase	,   bos_token_id=5_02_56	,   eos_token_id=5_02_56	,   )
		else:
				snake_case_					:				Optional[Any]    			=							GPTaConfig.from_json_file(args.config_file     )

		snake_case_					:				str    			=							["GPT2LMHeadModel"]

		# Convert.
		print("Converting"     )
		snake_case_					:				Optional[Any]    			=							convert_megatron_checkpoint(_UpperCamelCase	,   _UpperCamelCase	,   _UpperCamelCase     )

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

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

		snake_case_					:				Dict    			=							AutoTokenizer.from_pretrained(_UpperCamelCase     )
		snake_case_					:				Any    			=							type(_UpperCamelCase     ).__name__
		snake_case_					:				Tuple    			=							tokenizer_class

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

		# Save tokenizer based on args
		print(F'''Adding {tokenizer_class} tokenizer files'''     )
		tokenizer.save_pretrained(_UpperCamelCase     )

		# Store the state_dict to file.
		snake_case_					:				Optional[int]    			=							os.path.join(_UpperCamelCase	,   "pytorch_model.bin"     )
		print(F'''Saving checkpoint to \"{output_checkpoint_file}\"'''     )
		torch.save(_UpperCamelCase	,   _UpperCamelCase     )


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

if __name__ == "__main__":
							main()

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




"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




