






import argparse
from pathlib import Path

import torch

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


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




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						torch.load(__snake_case					,	map_location='cpu'  )
					if "model" in sd.keys():
										UpperCAmelCase_   :				str						=						torch.load(__snake_case					,	map_location='cpu'  )['model']

					# pop unnecessary weights
					UpperCAmelCase_   :				Optional[int]						=						[
					    'decoder.version',
					    'decoder.output_projection.weight',
					]
					for key in keys_to_delete:
										if key in sd:
															sd.pop(__snake_case  )

					UpperCAmelCase_   :				int						=						{
					    '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:
															UpperCAmelCase_   :				List[str]						=						sd.pop(__snake_case  )

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

															UpperCAmelCase_   :				List[Any]						=						key.replace('.qkv_proj.'					,	'.q_proj.'  )
															UpperCAmelCase_   :				Dict						=						key.replace('.qkv_proj.'					,	'.k_proj.'  )
															UpperCAmelCase_   :				Union[str, Any]						=						key.replace('.qkv_proj.'					,	'.v_proj.'  )

															UpperCAmelCase_   :				Union[str, Any]						=						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
															UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						torch.split(__snake_case					,	depth // 3					,	dim=0  )

															UpperCAmelCase_   :				Optional[Any]						=						q
															UpperCAmelCase_   :				List[Any]						=						k
															UpperCAmelCase_   :				Any						=						v
															del sd[key]

					return sd




@torch.no_grad()
def 					lowercase__				(					__snake_case   :							Dict					,	__snake_case   :							Any					,	__snake_case   :							Any=None  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						load_checkpoint(__snake_case  )

					if config is not None:
										UpperCAmelCase_   :				str						=						OPTConfig.from_pretrained(__snake_case  )
					else:
										UpperCAmelCase_   :				Optional[int]						=						OPTConfig()

					UpperCAmelCase_   :				Tuple						=						OPTModel(__snake_case  ).half().eval()
					model.load_state_dict(__snake_case  )

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


if __name__ == "__main__":
		__UpperCAmelCase     							=						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.')
		__UpperCAmelCase     							=						parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)














from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  '''umt5'''
		_snake_case   :   Union[str, Any]							=  ['''past_key_values''']



		def __init__(							self    ,		_UpperCamelCase=2_5_0_1_1_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=8    ,		_UpperCamelCase=None    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase="gated-gelu"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="T5Tokenizer"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCamelCase    ,		tokenizer_class=_UpperCamelCase    ,		tie_word_embeddings=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		decoder_start_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						vocab_size
							UpperCAmelCase_   :				List[str]						=						d_model
							UpperCAmelCase_   :				Any						=						d_kv
							UpperCAmelCase_   :				Optional[int]						=						d_ff
							UpperCAmelCase_   :				List[Any]						=						num_layers
							UpperCAmelCase_   :				Optional[Any]						=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCAmelCase_   :				Optional[int]						=						num_heads
							UpperCAmelCase_   :				Optional[int]						=						relative_attention_num_buckets
							UpperCAmelCase_   :				Dict						=						relative_attention_max_distance
							UpperCAmelCase_   :				Tuple						=						dropout_rate
							UpperCAmelCase_   :				Union[str, Any]						=						layer_norm_epsilon
							UpperCAmelCase_   :				Optional[int]						=						initializer_factor
							UpperCAmelCase_   :				List[str]						=						feed_forward_proj
							UpperCAmelCase_   :				Any						=						use_cache

							UpperCAmelCase_   :				List[Any]						=						self.feed_forward_proj.split('-'       )
							UpperCAmelCase_   :				List[Any]						=						act_info[-1]
							UpperCAmelCase_   :				Union[str, Any]						=						act_info[0] == 'gated'

							if len(_UpperCamelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase       ) > 2:
												raise ValueError(
												    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
												    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
												    '\'gated-gelu\' or \'relu\''       )

							if feed_forward_proj == "gated-gelu":
												UpperCAmelCase_   :				Optional[int]						=						'gelu_new'



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.d_model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.num_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return self.num_layers





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_   :				str						=						{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
												UpperCAmelCase_   :				Optional[int]						=						'past_encoder_sequence + sequence'
												UpperCAmelCase_   :				str						=						{0: 'batch'}
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'decoder_sequence'}
												UpperCAmelCase_   :				Union[str, Any]						=						{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
												self.fill_with_past_key_values_(_UpperCamelCase    ,		direction='inputs'       )

							return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_3



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 5E-4






