
"""simple docstring"""




import argparse
from collections import defaultdict

import yaml


_A     :			str  						=			"""docs/source/en/_toctree.yml"""


def     __magic_name__      ( __snake_case							:			List[str]    )					->							Union[str, Any]:
       lowercase       :							Optional[int]									=				defaultdict(__snake_case    )
       for doc in model_doc:
              counts[doc["local"]] += 1
       lowercase       :							Optional[Any]									=				[key for key, value in counts.items() if value > 1]

       lowercase       :							int									=				[]
       for duplicate_key in duplicates:
              lowercase       :							Union[str, Any]									=				list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}    )
              if len(__snake_case    ) > 1:
                     raise ValueError(
                         f"""{duplicate_key} is present several times in the documentation table of content at """
                         "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
                         "others."    )
              # Only add this once
              new_doc.append({"local": duplicate_key, "title": titles[0]}    )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]    )

       # Sort
       return sorted(__snake_case	,					key=lambda __snake_case    : s["title"].lower()    )


def     __magic_name__      ( __snake_case							:			Any=False    )					->							Optional[Any]:
       with open(__snake_case	,					encoding="utf-8"    ) as f:
              lowercase       :							str									=				yaml.safe_load(f.read()    )

       # Get to the API doc
       lowercase       :							Any									=				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowercase       :							Optional[int]									=				content[api_idx]["sections"]

       # Then to the model doc
       lowercase       :							Union[str, Any]									=				0
       while api_doc[model_idx]["title"] != "Models":
              model_idx += 1

       lowercase       :							Tuple									=				api_doc[model_idx]["sections"]

       lowercase       :							Optional[int]									=				[(idx, section) for idx, section in enumerate(__snake_case    ) if "sections" in section]
       lowercase       :							List[Any]									=				False
       for idx, modality_doc in modalities_docs:
              lowercase       :							Dict									=				modality_doc["sections"]
              lowercase       :							Tuple									=				clean_model_doc_toc(__snake_case    )

              if old_modality_doc != new_modality_doc:
                     lowercase       :							List[str]									=				True
                     if overwrite:
                            lowercase       :							List[str]									=				new_modality_doc

       if diff:
              if overwrite:
                     lowercase       :							List[str]									=				model_doc
                     lowercase       :							List[Any]									=				api_doc
                     with open(__snake_case	,					"w"	,					encoding="utf-8"    ) as f:
                            f.write(yaml.dump(__snake_case	,					allow_unicode=__snake_case    )    )
              else:
                     raise ValueError(
                         "The model doc part of the table of content is not properly sorted, run `make style` to fix this."    )


if __name__ == "__main__":
   _A     :			Any  						=			argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   _A     :			Any  						=			parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

       lowercase       :							int									=				{key.replace("decoder"	,					"model"    ): val for key, val in state_dict.items()}

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					max_position_embeddings=args.max_target_positions	,					num_layers=args.decoder_layers	,					attention_heads=args.decoder_attention_heads	,					ffn_dim=args.decoder_ffn_embed_dim	,					d_model=args.decoder_embed_dim	,					layerdrop=args.decoder_layerdrop	,					dropout=args.dropout	,					attention_dropout=args.attention_dropout	,					activation_dropout=args.activation_dropout	,					activation_function="gelu"	,					scale_embedding=not args.no_scale_embedding	,					tie_word_embeddings=args.share_decoder_input_output_embed	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   _A     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


