



"""simple docstring"""
import argparse
from pathlib import Path

import torch

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


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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Dict:
		a_     :      Any 						=     torch.load(__UpperCamelCase, map_location="cpu"	)
		if "model" in sd.keys():
				a_     :      Optional[Any] 						=     torch.load(__UpperCamelCase, map_location="cpu"	)["model"]

		# pop unnecessary weights
		a_     :      List[str] 						=     [
		    "decoder.version",
		    "decoder.output_projection.weight",
		]
		for key in keys_to_delete:
				if key in sd:
						sd.pop(__UpperCamelCase	)

		a_     :      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:
						a_     :      int 						=     sd.pop(__UpperCamelCase	)

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

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

						a_     :      List[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
						a_						,				a_						,				a_     :      List[str] 						=     torch.split(__UpperCamelCase, depth // 3, dim=0	)

						a_     :      Optional[Any] 						=     q
						a_     :      Optional[Any] 						=     k
						a_     :      Dict 						=     v
						del sd[key]

		return sd



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      Any:
		a_     :      Optional[Any] 						=     load_checkpoint(__UpperCamelCase	)

		if config is not None:
				a_     :      Any 						=     OPTConfig.from_pretrained(__UpperCamelCase	)
		else:
				a_     :      Dict 						=     OPTConfig()

		a_     :      Optional[Any] 						=     OPTModel(__UpperCamelCase	).half().eval()
		model.load_state_dict(__UpperCamelCase	)

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


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





"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ViltImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."			,     a_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."      )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."      )

									super().__init__(a_			,     a_      )
									a_     :      Dict 						=     self.image_processor






							def __call__(   self			,     a_			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     self.tokenizer(
									    text=a_			,     add_special_tokens=a_			,     padding=a_			,     truncation=a_			,     max_length=a_			,     stride=a_			,     pad_to_multiple_of=a_			,     return_token_type_ids=a_			,     return_attention_mask=a_			,     return_overflowing_tokens=a_			,     return_special_tokens_mask=a_			,     return_offsets_mapping=a_			,     return_length=a_			,     verbose=a_			,     return_tensors=a_			,     **a_			,     )
									# add pixel_values + pixel_mask
									a_     :      List[Any] 						=     self.image_processor(a_			,     return_tensors=a_      )
									encoding.update(a_      )

									return encoding






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.tokenizer.model_input_names
									a_     :      Dict 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor