


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


a			:			int												=					logging.get_logger(__name__)

a			:			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'''







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






					def __init__(					self			,							snake_case_=2_5_0_8_8_0			,							snake_case_=6_4			,							snake_case_=2			,							snake_case_=8			,							snake_case_=1e-5			,							snake_case_=0.0_2			,							snake_case_=True			,							snake_case_=1			,							snake_case_=2			,							snake_case_=False			,							snake_case_=0.0			,							snake_case_=0.0			,							snake_case_=1			,							snake_case_=False			,							**snake_case_			,							) ->      Tuple:







								'''simple docstring'''

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

								__lowercase			       = bos_token_id
								__lowercase			       = eos_token_id
								__lowercase			       = slow_but_exact

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




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					version.parse("1.12"					)






					def __init__(					self			,							snake_case_			,							snake_case_ = "default"			,							snake_case_ = None			,							snake_case_ = False			,							) ->      str:







								'''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?
											__lowercase			       = 0






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







								'''simple docstring'''

								__lowercase			       = 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_    )
											__lowercase			       = {0: '''batch''', 1: '''past_sequence + sequence'''}
								else:
											__lowercase			       = {0: '''batch''', 1: '''sequence'''}

								return common_inputs






					@property
					def A   (					self    ) ->      int:







								'''simple docstring'''

								return self._config.n_layer






					@property
					def A   (					self    ) ->      int:







								'''simple docstring'''

								return self._config.n_head






					@property
					def A   (					self    ) ->      float:







								'''simple docstring'''

								return 1e-3






					def A   (					self			,							snake_case_			,							snake_case_ = -1			,							snake_case_ = -1			,							snake_case_ = False			,							snake_case_ = None			,							) ->      Mapping[str, Any]:







								'''simple docstring'''

								__lowercase			       = 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()
								__lowercase			       = 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

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

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

								return ordered_inputs






					@property
					def A   (					self    ) ->      int:







								'''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)


