
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
							raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
lowercase__		:int   =    logging.get_logger(__name__)

lowercase__		:Union[str, Any]   =    "Hello world! cécé herlolip"



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						FairseqRobertaModel.from_pretrained(lowerCAmelCase__		)
					roberta.eval()  # disable dropout
					lowercase							=						roberta.model.encoder.sentence_encoder
					lowercase							=						XLMRobertaConfig(
					    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,			hidden_size=roberta.cfg.model.encoder_embed_dim ,			num_hidden_layers=roberta.cfg.model.encoder_layers ,			num_attention_heads=roberta.cfg.model.encoder_attention_heads ,			intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,			max_position_embeddings=514 ,			type_vocab_size=1 ,			layer_norm_eps=1E-5 ,			)
					if classification_head:
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

					print('''Our RoBERTa config:''' ,			lowerCAmelCase__		)

					lowercase							=						XLMRobertaXLForSequenceClassification(lowerCAmelCase__		) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__		)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowercase							=						roberta_sent_encoder.embed_tokens.weight
					lowercase							=						roberta_sent_encoder.embed_positions.weight
					lowercase							=						torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.

					lowercase							=						roberta_sent_encoder.layer_norm.weight
					lowercase							=						roberta_sent_encoder.layer_norm.bias

					for i in range(config.num_hidden_layers		):
										# Encoder: start of layer
										lowercase							=						model.roberta.encoder.layer[i]
										lowercase							=						roberta_sent_encoder.layers[i]

										lowercase							=						layer.attention
										lowercase							=						roberta_layer.self_attn_layer_norm.weight
										lowercase							=						roberta_layer.self_attn_layer_norm.bias

										# self attention
										lowercase							=						layer.attention.self
										assert (
										    roberta_layer.self_attn.k_proj.weight.data.shape
										    == roberta_layer.self_attn.q_proj.weight.data.shape
										    == roberta_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size)		)
										)

										lowercase							=						roberta_layer.self_attn.q_proj.weight
										lowercase							=						roberta_layer.self_attn.q_proj.bias
										lowercase							=						roberta_layer.self_attn.k_proj.weight
										lowercase							=						roberta_layer.self_attn.k_proj.bias
										lowercase							=						roberta_layer.self_attn.v_proj.weight
										lowercase							=						roberta_layer.self_attn.v_proj.bias

										# self-attention output
										lowercase							=						layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										lowercase							=						roberta_layer.self_attn.out_proj.weight
										lowercase							=						roberta_layer.self_attn.out_proj.bias

										# this one is final layer norm
										lowercase							=						roberta_layer.final_layer_norm.weight
										lowercase							=						roberta_layer.final_layer_norm.bias

										# intermediate
										lowercase							=						layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										lowercase							=						roberta_layer.fca.weight
										lowercase							=						roberta_layer.fca.bias

										# output
										lowercase							=						layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										lowercase							=						roberta_layer.fca.weight
										lowercase							=						roberta_layer.fca.bias
										# end of layer

					if classification_head:
										lowercase							=						roberta.model.classification_heads['''mnli'''].dense.weight
										lowercase							=						roberta.model.classification_heads['''mnli'''].dense.bias
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.weight
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										lowercase							=						roberta.model.encoder.lm_head.dense.weight
										lowercase							=						roberta.model.encoder.lm_head.dense.bias
										lowercase							=						roberta.model.encoder.lm_head.layer_norm.weight
										lowercase							=						roberta.model.encoder.lm_head.layer_norm.bias
										lowercase							=						roberta.model.encoder.lm_head.weight
										lowercase							=						roberta.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					lowercase							=						roberta.encode(lowerCAmelCase__		).unsqueeze(0		)  # batch of size 1

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

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


if __name__ == "__main__":
							lowercase__		:Dict   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--roberta_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."
							)
							lowercase__		:Optional[int]   =    parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)









"""simple docstring"""

import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

lowercase__    :  Dict							    =	[
    '''word_embeddings_layernorm.weight''',
    '''word_embeddings_layernorm.bias''',
    '''input_layernorm.weight''',
    '''input_layernorm.bias''',
    '''post_attention_layernorm.weight''',
    '''post_attention_layernorm.bias''',
    '''self_attention.dense.bias''',
    '''mlp.dense_4h_to_h.bias''',
    '''ln_f.weight''',
    '''ln_f.bias''',
]

lowercase__    :  str							    =	[
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     Optional[int]				=	{
        '''word_embeddings.weight''': '''word_embeddings.weight''',
        '''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
        '''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
        '''weight''': '''ln_f.weight''',
        '''bias''': '''ln_f.bias''',
    }

    if key in layer_rename_map:
        return layer_rename_map[key]

    # Handle transformer blocks
    snake_case_ :     List[Any]				=	int(re.match(r'''.*layer_(\d*).*'''		,			_a    )[1]    )
    layer_number -= 3
    return f"h.{layer_number}." + key




def __lowercase			(							_a    ):
    if dtype == torch.bool:
        return 1 / 8
    snake_case_ :     Dict				=	re.search(r'''[^\d](\d+)$'''		,			str(_a    )    )
    if bit_search is None:
        raise ValueError(f"`dtype` is not a valid dtype: {dtype}."    )
    snake_case_ :     Optional[int]				=	int(bit_search.groups()[0]    )
    return bit_size // 8




def __lowercase			(							_a		,			_a		,			_a		,			_a		,			_a    ):
    # Construct model
    if bloom_config_file == "":
        snake_case_ :     int				=	BloomConfig()
    else:
        snake_case_ :     List[str]				=	BloomConfig.from_json_file(_a    )

    if shard_model:
        snake_case_ :     List[str]				=	os.listdir(_a    )
        snake_case_ :     int				=	sorted(filter(lambda _a    : s.startswith('''layer'''    ) and "model_00" in s		,			_a    )    )

        snake_case_ :     List[str]				=	{'''weight_map''': {}, '''metadata''': {}}
        snake_case_ :     Any				=	0

        snake_case_ :     Union[str, Any]				=	None

        snake_case_ :     List[str]				=	BloomConfig()

        for j, file in enumerate(_a    ):
            print('''Processing file: {}'''.format(_a    )    )
            snake_case_ :     Dict				=	None

            for i in range(_a    ):
                # load all TP files
                snake_case_ :     Union[str, Any]				=	file.replace('''model_00'''		,			f"model_0{i}"    )
                snake_case_ :     List[str]				=	torch.load(os.path.join(_a		,			_a    )		,			map_location='''cpu'''    )

                # Rename keys in the transformers names
                snake_case_ :     str				=	list(temp.keys()    )
                for key in keys:
                    snake_case_ :     Any				=	temp.pop(_a    )

                if tensors is None:
                    snake_case_ :     Any				=	temp
                else:
                    for key in tensors.keys():
                        if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            snake_case_ :     Tuple				=	1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN    ) else 0
                            # We concatenate these weights accross TP ranks
                            snake_case_ :     List[str]				=	torch.cat([tensors[key], temp[key]]		,			dim=_a    )

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                    snake_case_ :     Any				=	tensors[key] / pretraining_tp
            torch.save(
                _a		,			os.path.join(
                    _a		,			'''pytorch_model_{}-of-{}.bin'''.format(str(j + 1    ).zfill(5    )		,			str(len(_a    )    ).zfill(5    )    )		,			)		,			)

            for key in tensors.keys():
                snake_case_ :     List[str]				=	tensors[key]
                total_size += value.numel() * get_dtype_size(value.dtype    )
                if key not in index_dict["weight_map"]:
                    snake_case_ :     List[str]				=	'''pytorch_model_{}-of-{}.bin'''.format(
                        str(j + 1    ).zfill(5    )		,			str(len(_a    )    ).zfill(5    )    )

        snake_case_ :     int				=	BloomConfig()
        snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        snake_case_ :     Dict				=	total_size
        with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
            f.write(config.to_json_string()    )
        with open(os.path.join(_a		,			WEIGHTS_NAME + '''.index.json'''    )		,			'''w'''		,			encoding='''utf-8'''    ) as f:
            snake_case_ :     Tuple				=	json.dumps(_a		,			indent=2		,			sort_keys=_a    ) + '''\n'''
            f.write(_a    )
    else:
        snake_case_ :     Union[str, Any]				=	BloomModel(_a    )

        snake_case_ :     List[str]				=	os.listdir(_a    )
        snake_case_ :     Dict				=	sorted(filter(lambda _a    : s.startswith('''layer'''    ) and "model_00" in s		,			_a    )    )

        snake_case_ :     List[Any]				=	None
        for i, file in enumerate(_a    ):
            snake_case_ :     Optional[Any]				=	None
            for i in range(_a    ):
                # load all TP files
                snake_case_ :     List[str]				=	file.replace('''model_00'''		,			f"model_0{i}"    )
                snake_case_ :     Optional[Any]				=	torch.load(os.path.join(_a		,			_a    )		,			map_location='''cpu'''    )

                # Rename keys in the transformers names
                snake_case_ :     str				=	list(temp.keys()    )
                for key in keys:
                    snake_case_ :     str				=	temp.pop(_a    )

                if tensors is None:
                    snake_case_ :     int				=	temp
                else:
                    for key in tensors.keys():
                        # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                        if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            snake_case_ :     Tuple				=	1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN    ) else 0
                            # We concatenate these weights accross TP ranks
                            snake_case_ :     Optional[Any]				=	torch.cat([tensors[key], temp[key]]		,			dim=_a    )

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                    snake_case_ :     Union[str, Any]				=	tensors[key] / pretraining_tp

            snake_case_ :     Any				=	model.load_state_dict(_a		,			strict=_a    )
            assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
            if missing_keys is None:
                snake_case_ :     Optional[int]				=	set(other_keys.missing_keys    )
            else:
                snake_case_ :     Tuple				=	missing_keys.intersection(set(other_keys.missing_keys    )    )

        assert not missing_keys, f"The keys {missing_keys} are missing"

        # Save pytorch-model
        os.makedirs(_a		,			exist_ok=_a    )
        snake_case_ :     List[str]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
        snake_case_ :     Optional[Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}"    )
        if config.torch_dtype is not None:
            snake_case_ :     Optional[Any]				=	model.to(config.torch_dtype    )
        torch.save(model.state_dict()		,			_a    )
        print(f"Save configuration file to {pytorch_config_dump_path}"    )
        with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
            f.write(config.to_json_string()    )


if __name__ == "__main__":
     lowercase__    :  str							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--bloom_checkpoint_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the Megatron-LM checkpoint path.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--bloom_config_file''',
         default='''''',
         type=str,
         help=(
             '''An optional config json file corresponding to the pre-trained model. \n'''
             '''This specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--shard_model''',
         action='''store_true''',
         help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
     )
     parser.add_argument(
         '''--pretraining_tp''',
         default=4,
         type=int,
         help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
     )
     lowercase__    :  List[Any]							    =	parser.parse_args()
     convert_bloom_checkpoint_to_pytorch(
         args.bloom_checkpoint_path,
         args.bloom_config_file,
         args.pytorch_dump_folder_path,
         args.shard_model,
         args.pretraining_tp,
     )





