




"""simple docstring"""




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)

lowerCAmelCase							:			List[str]     							=							{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						"sew-d"





				def __init__(       self		, _a=32		, _a=768		, _a=12		, _a=12		, _a=3_072		, _a=2		, _a=512		, _a=256		, _a=True		, _a=True		, _a=("p2c", "c2p")		, _a="layer_norm"		, _a="gelu_python"		, _a=0.1		, _a=0.1		, _a=0.1		, _a=0.0		, _a=0.1		, _a=0.02		, _a=1e-7		, _a=1e-5		, _a="group"		, _a="gelu"		, _a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		, _a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		, _a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		, _a=False		, _a=128		, _a=16		, _a=True		, _a=0.05		, _a=10		, _a=2		, _a=0.0		, _a=10		, _a=0		, _a="mean"		, _a=False		, _a=False		, _a=256		, _a=0		, _a=1		, _a=2		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a		, pad_token_id=_a		, bos_token_id=_a		, eos_token_id=_a )
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					feat_extract_norm
							lowerCamelCase			      =					feat_extract_activation
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					conv_bias
							lowerCamelCase			      =					num_conv_pos_embeddings
							lowerCamelCase			      =					num_conv_pos_embedding_groups
							lowerCamelCase			      =					len(self.conv_dim )
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					squeeze_factor
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					position_buckets
							lowerCamelCase			      =					share_att_key
							lowerCamelCase			      =					relative_attention
							lowerCamelCase			      =					norm_rel_ebd
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					hidden_dropout
							lowerCamelCase			      =					attention_dropout
							lowerCamelCase			      =					activation_dropout
							lowerCamelCase			      =					feat_proj_dropout
							lowerCamelCase			      =					final_dropout
							lowerCamelCase			      =					layer_norm_eps
							lowerCamelCase			      =					feature_layer_norm_eps
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					vocab_size

							if (
							    (len(self.conv_stride ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    """Configuration for convolutional layers is incorrect."""
										    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
										    f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
										    f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCamelCase			      =					apply_spec_augment
							lowerCamelCase			      =					mask_time_prob
							lowerCamelCase			      =					mask_time_length
							lowerCamelCase			      =					mask_time_min_masks
							lowerCamelCase			      =					mask_feature_prob
							lowerCamelCase			      =					mask_feature_length
							lowerCamelCase			      =					mask_feature_min_masks

							# ctc loss
							lowerCamelCase			      =					ctc_loss_reduction
							lowerCamelCase			      =					ctc_zero_infinity

							# sequence classification
							lowerCamelCase			      =					use_weighted_layer_sum
							lowerCamelCase			      =					classifier_proj_size





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return functools.reduce(operator.mul		, self.conv_stride		, 1 )









"""simple docstring"""




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """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:
									lowerCamelCase			      =					sd.pop(snake_case__				)

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

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					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
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

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

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					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__":
					lowerCAmelCase							:			List[str]     							=							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.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



