
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			,   snake_case				: Any=0			)   ->     List[str]:



						'''simple docstring'''

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

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




def 						A_			(				snake_case				: Optional[int]			,   snake_case				: List[Any]			,   snake_case				: Dict			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						__UpperCamelCase  	=      (num_heads, num_splits, hidden_size) + input_shape[1:]
						__UpperCamelCase  	=      param.view(*snake_case			)
						__UpperCamelCase  	=      param.transpose(0			,   1			).contiguous()
						__UpperCamelCase  	=      param.view(*snake_case			)
						return param




def 						A_			(				snake_case				: str			,   snake_case				: str			)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      config.n_positions
						__UpperCamelCase  	=      config.n_layer
						__UpperCamelCase  	=      config.vocab_size
						__UpperCamelCase  	=      config.n_head
						__UpperCamelCase  	=      config.n_embd // config.n_head

						__UpperCamelCase  	=      sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
						__UpperCamelCase  	=      {
						    '''transformer.wte.weight''': word_embeddings,
						    '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
						    '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
						    '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
						}

						__UpperCamelCase  	=      '''model.language_model.encoder.layers.'''
						for i in range(snake_case			):
												__UpperCamelCase  	=      torch.tril(torch.ones((n_positions, n_positions)			,   dtype=torch.bool			)			)
												__UpperCamelCase  	=      causal_mask.view(1			,   1			,   snake_case			,   snake_case			)
												__UpperCamelCase  	=      causal_mask
												__UpperCamelCase  	=      torch.tensor(-1e4			,   dtype=torch.bfloataa			)

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.input_layernorm.weight"]
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.input_layernorm.bias"]

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.weight"]
												__UpperCamelCase  	=      fix_query_key_value_ordering(snake_case			,   3			,   snake_case			,   snake_case			)
												__UpperCamelCase  	=      vala.transpose(0			,   1			).contiguous()

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.bias"]
												__UpperCamelCase  	=      fix_query_key_value_ordering(snake_case			,   3			,   snake_case			,   snake_case			)
												__UpperCamelCase  	=      vala

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.self_attention.dense.weight"].transpose(
												    0			,   1			)
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.self_attention.dense.bias"]
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.post_attention_layernorm.weight"]
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.post_attention_layernorm.bias"]
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.weight"].transpose(0			,   1			)
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.bias"]
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.weight"].transpose(
												    0			,   1			)
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.bias"]

						# For LM head, transformers' wants the matrix to weight embeddings.
						__UpperCamelCase  	=      word_embeddings

						return sd_hf




def 						A_			(				snake_case				: Tuple			,   snake_case				: str			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      64000
						__UpperCamelCase  	=      config_megatron['''encoder_seq_length''']
						__UpperCamelCase  	=      config_megatron['''hidden_size''']
						__UpperCamelCase  	=      config_megatron['''num_layers''']
						__UpperCamelCase  	=      config_megatron['''num_attention_heads''']
						__UpperCamelCase  	=      config_megatron['''ffn_hidden_size''']
						__UpperCamelCase  	=      '''gelu'''
						__UpperCamelCase  	=      0.1
						__UpperCamelCase  	=      0.1
						__UpperCamelCase  	=      0.1
						__UpperCamelCase  	=      config_megatron['''layernorm_epsilon''']  # 1e-5
						__UpperCamelCase  	=      config_megatron['''init_method_std''']  # 0.02
						__UpperCamelCase  	=      config_megatron['''apply_query_key_layer_scaling''']  # True
						__UpperCamelCase  	=      True
						__UpperCamelCase  	=      True

						# This identifies the 6.7B (7B) model which uses a different tokenizer
						if config_megatron["hidden_size"] == 4096:
												__UpperCamelCase  	=      1  # <|endoftext|>
												__UpperCamelCase  	=      1  # <|endoftext|>
												__UpperCamelCase  	=      0  # <unk>
						else:
												__UpperCamelCase  	=      2  # <s>
												__UpperCamelCase  	=      3  # <|endoftext|>
												__UpperCamelCase  	=      0  # <pad>

						return config_hf




def 						A_			(				snake_case				: str			)   ->     Any:



						'''simple docstring'''

						print(snake_case			)

						__UpperCamelCase  	=      args.checkpoint_path
						__UpperCamelCase  	=      args.save_path
						if isfile(snake_case			):
												raise FileNotFoundError(f"ERROR! could not find file {checkpoint_path}"			)

						# Load the model.
						__UpperCamelCase  	=      torch.load(snake_case			,   map_location='''cpu'''			)

						# Load the config.
						__UpperCamelCase  	=      checkpoint['''hyper_parameters''']['''cfg''']
						__UpperCamelCase  	=      GPTaConfig()
						__UpperCamelCase  	=      copy_config(config_hf=snake_case			,   config_megatron=snake_case			)
						__UpperCamelCase  	=      ['''GPT2LMHeadModel''']

						__UpperCamelCase  	=      checkpoint['''state_dict''']

						# Convert.
						print('''Converting'''			)
						__UpperCamelCase  	=      convert_megatron_checkpoint(snake_case			,   snake_case			)

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

						__UpperCamelCase  	=      '''GPTSw3Tokenizer'''

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

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


if __name__ == "__main__":
				lowercase__				:				Optional[Any]						=      argparse.ArgumentParser()
				parser.add_argument(
				    "--checkpoint_path",
				    type=str,
				    required=True,
				    help="e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000",
				)
				parser.add_argument("--save_path", type=str, required=True, help="e.g. /home/user/gpt-sw3/hf")
				parser.add_argument("--print-checkpoint-structure", action="store_true")
				lowercase__				:				List[Any]						=      parser.parse_args()
				main(_args)






"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
			raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
__A					=			logging.get_logger(__name__)

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings       ,					hidden_size=dataavec_model.args.encoder_embed_dim       ,					num_hidden_layers=dataavec_model.args.encoder_layers       ,					num_attention_heads=dataavec_model.args.encoder_attention_heads       ,					intermediate_size=dataavec_model.args.encoder_ffn_embed_dim       ,					max_position_embeddings=514       ,					type_vocab_size=1       ,					layer_norm_eps=1E-5       ,					)
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers			):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase			)[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase			)[0]
	print(our_output.shape       ,					their_output.shape			)
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output			)			).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
	_lowerCAmelCase								=torch.allclose(__UpperCamelCase       ,					__UpperCamelCase       ,					atol=1E-3			)
	print("""Do both models output the same tensors?"""       ,					"""🔥""" if success else """💩"""			)
	if not success:
		raise Exception("""Something went wRoNg"""			)

	pathlib.Path(__UpperCamelCase			).mkdir(parents=__UpperCamelCase       ,					exist_ok=__UpperCamelCase			)
	print(F'''Saving model to {pytorch_dump_folder_path}'''			)
	model.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
			__A					=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			__A					=			parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



