


'''simple docstring'''
import argparse
from pathlib import Path

import torch

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


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


def     a   (    __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				torch.load(__a				,      map_location='''cpu'''     )
					if "model" in sd.keys():
										UpperCamelCase__     :Dict							      =				torch.load(__a				,      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(__a     )

					UpperCamelCase__     :Dict							      =				{
					    '''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(__a     )

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

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

															UpperCamelCase__     :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
															UpperCamelCase__     :List[Any]							      =				torch.split(__a				,      depth // 3				,      dim=0     )

															UpperCamelCase__     :List[str]							      =				q
															UpperCamelCase__     :Optional[int]							      =				k
															UpperCamelCase__     :Optional[int]							      =				v
															del sd[key]

					return sd


@torch.no_grad()
def     a   (    __a				,      __a				,      __a=None     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				load_checkpoint(__a     )

					if config is not None:
										UpperCamelCase__     :int							      =				OPTConfig.from_pretrained(__a     )
					else:
										UpperCamelCase__     :str							      =				OPTConfig()

					UpperCamelCase__     :Dict							      =				OPTModel(__a     ).half().eval()
					model.load_state_dict(__a     )

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


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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case							   =  {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''LayoutLMv3FeatureExtractor''']
		__snake_case							   =  ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__snake_case							   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)