

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
   from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


_lowerCamelCase      :					int					    =      logging.get_logger(__name__)

_lowerCamelCase      :					Dict					    =      {
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}





class        lowerCAmelCase__  (					lowerCAmelCase__			):



       '''simple docstring'''







       lowercase_						=    """bloom"""
       lowercase_						=    ["""past_key_values"""]
       lowercase_						=    {
           """num_hidden_layers""": """n_layer""",
           """num_attention_heads""": """n_head""",
       }

       def __init__(							self ,   lowercase__=2_5_0_8_8_0 ,   lowercase__=6_4 ,   lowercase__=2 ,   lowercase__=8 ,   lowercase__=1E-5 ,   lowercase__=0.02 ,   lowercase__=True ,   lowercase__=1 ,   lowercase__=2 ,   lowercase__=False ,   lowercase__=0.0 ,   lowercase__=0.0 ,   lowercase__=1 ,   lowercase__=False ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =vocab_size
             # Backward compatibility with n_embed kwarg
             __A    =kwargs.pop('''n_embed''' ,   snake_case_     )
             __A    =hidden_size if n_embed is None else n_embed
             __A    =n_layer
             __A    =n_head
             __A    =layer_norm_epsilon
             __A    =initializer_range
             __A    =use_cache
             __A    =pretraining_tp
             __A    =apply_residual_connection_post_layernorm
             __A    =hidden_dropout
             __A    =attention_dropout

             __A    =bos_token_id
             __A    =eos_token_id
             __A    =slow_but_exact

             super().__init__(bos_token_id=snake_case_ ,   eos_token_id=snake_case_ ,   **snake_case_     )





class        lowerCAmelCase__  (					lowerCAmelCase__			):



       '''simple docstring'''







       lowercase_						=    version.parse("""1.12"""			)

       def __init__(							self ,   lowercase__ ,   lowercase__ = "default" ,   lowercase__ = None ,   lowercase__ = False ,   ):







             '''simple docstring'''



             super().__init__(snake_case_ ,   task=snake_case_ ,   patching_specs=snake_case_ ,   use_past=snake_case_     )
             if not getattr(self._config ,   '''pad_token_id''' ,   snake_case_     ):
                   # TODO: how to do that better?
                   __A    =0

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}     )
             if self.use_past:
                   # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
                   self.fill_with_past_key_values_(snake_case_ ,   direction='''inputs''' ,   inverted_values_shape=snake_case_     )
                   __A    ={0: '''batch''', 1: '''past_sequence + sequence'''}
             else:
                   __A    ={0: '''batch''', 1: '''sequence'''}

             return common_inputs

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._config.n_layer

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._config.n_head

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1E-3

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = -1 ,   lowercase__ = -1 ,   lowercase__ = False ,   lowercase__ = None ,   ):







             '''simple docstring'''



             __A    =super(snake_case_ ,   self     ).generate_dummy_inputs(
                 snake_case_ ,   batch_size=snake_case_ ,   seq_length=snake_case_ ,   is_pair=snake_case_ ,   framework=snake_case_     )

             # We need to order the input in the way they appears in the forward()
             __A    =OrderedDict({'''input_ids''': common_inputs['''input_ids''']}     )

             # Need to add the past_keys
             if self.use_past:
                   if not is_torch_available():
                         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
                   else:
                         import torch

                         __A    ,		__A    =common_inputs['''input_ids'''].shape
                         # Not using the same length for past_key_values
                         __A    =seqlen + 2
                         __A    =self._config.hidden_size // self.num_attention_heads
                         __A    =(
                             batch * self.num_attention_heads,
                             head_dim,
                             past_key_values_length,
                         )
                         __A    =(
                             batch * self.num_attention_heads,
                             past_key_values_length,
                             head_dim,
                         )
                         __A    =[
                             (torch.zeros(snake_case_     ), torch.zeros(snake_case_     )) for _ in range(self.num_layers     )
                         ]

             __A    =common_inputs['''attention_mask''']
             if self.use_past:
                   __A    =ordered_inputs['''attention_mask'''].dtype
                   __A    =torch.cat(
                       [ordered_inputs['''attention_mask'''], torch.ones(snake_case_ ,   snake_case_ ,   dtype=snake_case_     )] ,   dim=1     )

             return ordered_inputs


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1_3






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


