
'''simple docstring'''






import argparse
from pathlib import Path

import torch

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


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





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Union[str, Any]									= torch.load(UpperCAmelCase__       ,							map_location='cpu')
				if "model" in sd.keys():
								lowerCamelCase					:			Optional[Any]									= torch.load(UpperCAmelCase__       ,							map_location='cpu')['model']

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

				lowerCamelCase					:			Optional[Any]									= {
				    '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					:			int									= sd.pop(UpperCAmelCase__)

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

												lowerCamelCase					:			List[str]									= key.replace('.qkv_proj.'       ,							'.q_proj.')
												lowerCamelCase					:			Optional[int]									= key.replace('.qkv_proj.'       ,							'.k_proj.')
												lowerCamelCase					:			Dict									= key.replace('.qkv_proj.'       ,							'.v_proj.')

												lowerCamelCase					:			str									= 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					:			str									= torch.split(UpperCAmelCase__       ,							depth // 3       ,							dim=0)

												lowerCamelCase					:			Optional[Any]									= q
												lowerCamelCase					:			Tuple									= k
												lowerCamelCase					:			Any									= v
												del sd[key]

				return sd





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							List[str]=None):
				lowerCamelCase					:			int									= load_checkpoint(UpperCAmelCase__)

				if config is not None:
								lowerCamelCase					:			Dict									= OPTConfig.from_pretrained(UpperCAmelCase__)
				else:
								lowerCamelCase					:			Tuple									= OPTConfig()

				lowerCamelCase					:			List[str]									= 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			      =      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			      =      parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


'''simple docstring'''






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A			      =      logging.get_logger(__name__)

A			      =      {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''segformer'''
					def __init__(     self,    A=3,    A=4,    A=[2, 2, 2, 2],    A=[8, 4, 2, 1],    A=[32, 64, 160, 256],    A=[7, 3, 3, 3],    A=[4, 2, 2, 2],    A=[1, 2, 5, 8],    A=[4, 4, 4, 4],    A="gelu",    A=0.0,    A=0.0,    A=0.1,    A=0.02,    A=0.1,    A=1e-6,    A=256,    A=255,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
													warnings.warn(
													    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
													    ' removed, as the behaviour will default to that of reshape_last_stage = True.',    A,    )

									lowerCamelCase					:			Tuple									= num_channels
									lowerCamelCase					:			Dict									= num_encoder_blocks
									lowerCamelCase					:			Dict									= depths
									lowerCamelCase					:			Any									= sr_ratios
									lowerCamelCase					:			List[Any]									= hidden_sizes
									lowerCamelCase					:			List[Any]									= patch_sizes
									lowerCamelCase					:			Tuple									= strides
									lowerCamelCase					:			int									= mlp_ratios
									lowerCamelCase					:			Dict									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			Tuple									= hidden_dropout_prob
									lowerCamelCase					:			Optional[int]									= attention_probs_dropout_prob
									lowerCamelCase					:			List[str]									= classifier_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= initializer_range
									lowerCamelCase					:			Union[str, Any]									= drop_path_rate
									lowerCamelCase					:			int									= layer_norm_eps
									lowerCamelCase					:			Any									= decoder_hidden_size
									lowerCamelCase					:			Optional[int]									= kwargs.get('reshape_last_stage',    A      )
									lowerCamelCase					:			Any									= semantic_loss_ignore_index





class     __snake_case		(   a__):
					_lowerCAmelCase			     =	version.parse('''1.11''')
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 1e-4




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
