




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase							:			List[Any]     							=							{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:			List[str]     							=							["""ReformerTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:			Any     							=							["""ReformerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:			List[str]     							=							[
					    """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ReformerAttention""",
					    """ReformerForMaskedLM""",
					    """ReformerForQuestionAnswering""",
					    """ReformerForSequenceClassification""",
					    """ReformerLayer""",
					    """ReformerModel""",
					    """ReformerModelWithLMHead""",
					    """ReformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_reformer import ReformerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_reformer_fast import ReformerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_reformer import (
										    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ReformerAttention,
										    ReformerForMaskedLM,
										    ReformerForQuestionAnswering,
										    ReformerForSequenceClassification,
										    ReformerLayer,
										    ReformerModel,
										    ReformerModelWithLMHead,
										    ReformerPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:			str     							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""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)



