


'''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__       ):
			UpperCAmelCase__	:       List[Any]   =  torch.load(_SCREAMING_SNAKE_CASE			,   map_location="""cpu"""       )
			if "model" in sd.keys():
						UpperCAmelCase__	:       Optional[int]   =  torch.load(_SCREAMING_SNAKE_CASE			,   map_location="""cpu"""       )['model']

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

			UpperCAmelCase__	:       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:
									UpperCAmelCase__	:       Union[str, Any]   =  sd.pop(_SCREAMING_SNAKE_CASE       )

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

									UpperCAmelCase__	:       Any   =  key.replace(""".qkv_proj."""			,   """.q_proj."""       )
									UpperCAmelCase__	:       Optional[Any]   =  key.replace(""".qkv_proj."""			,   """.k_proj."""       )
									UpperCAmelCase__	:       List[str]   =  key.replace(""".qkv_proj."""			,   """.v_proj."""       )

									UpperCAmelCase__	:       Optional[int]   =  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__	:       List[str]   =  torch.split(_SCREAMING_SNAKE_CASE			,   depth // 3			,   dim=0       )

									UpperCAmelCase__	:       Optional[int]   =  q
									UpperCAmelCase__	:       Optional[Any]   =  k
									UpperCAmelCase__	:       Union[str, Any]   =  v
									del sd[key]

			return sd







@torch.no_grad()
def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__=None       ):
			UpperCAmelCase__	:       Dict   =  load_checkpoint(_SCREAMING_SNAKE_CASE       )

			if config is not None:
						UpperCAmelCase__	:       str   =  OPTConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
			else:
						UpperCAmelCase__	:       List[str]   =  OPTConfig()

			UpperCAmelCase__	:       Tuple   =  OPTModel(_SCREAMING_SNAKE_CASE       ).half().eval()
			model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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


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"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         super().__init__()
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :Any											=				attention_head_dim
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads * attention_head_dim

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

         # 3. Define transformers blocks
         lowerCAmelCase__     :List[Any]											=				nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					dropout=__UpperCAmelCase       ,					cross_attention_dim=__UpperCAmelCase       ,					activation_fn=__UpperCAmelCase       ,					attention_bias=__UpperCAmelCase       ,					double_self_attention=__UpperCAmelCase       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
                 for d in range(__UpperCAmelCase )
             ] )

         lowerCAmelCase__     :List[Any]											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

         lowerCAmelCase__     :Union[str, Any]											=				hidden_states[None, :].reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				hidden_states.permute(0       ,					2       ,					1       ,					3       ,					4 )

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

         # 3. Output
         lowerCAmelCase__     :Any											=				self.proj_out(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				(
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             .permute(0       ,					3       ,					4       ,					1       ,					2 )
             .contiguous()
         )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
