




'''simple docstring'''
import argparse
from collections import defaultdict

import yaml


a_											=						'docs/source/en/_toctree.yml'

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						defaultdict(__lowerCAmelCase       )
    for doc in model_doc:
        counts[doc["local"]] += 1
    snake_case_ :    str     			=						[key for key, value in counts.items() if value > 1]

    snake_case_ :    Any     			=						[]
    for duplicate_key in duplicates:
        snake_case_ :    Dict     			=						list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}       )
        if len(__lowerCAmelCase       ) > 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(__lowerCAmelCase,		key=lambda __SCREAMING_SNAKE_CASE       : s["title"].lower()       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    with open(__lowerCAmelCase,		encoding="utf-8"       ) as f:
        snake_case_ :    Optional[Any]     			=						yaml.safe_load(f.read()       )

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

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

    snake_case_ :    str     			=						api_doc[model_idx]["sections"]

    snake_case_ :    Optional[int]     			=						[(idx, section) for idx, section in enumerate(__lowerCAmelCase       ) if "sections" in section]
    snake_case_ :    List[str]     			=						False
    for idx, modality_doc in modalities_docs:
        snake_case_ :    Dict     			=						modality_doc["sections"]
        snake_case_ :    Dict     			=						clean_model_doc_toc(__lowerCAmelCase       )

        if old_modality_doc != new_modality_doc:
            snake_case_ :    Union[str, Any]     			=						True
            if overwrite:
                snake_case_ :    Tuple     			=						new_modality_doc

    if diff:
        if overwrite:
            snake_case_ :    Dict     			=						model_doc
            snake_case_ :    List[str]     			=						api_doc
            with open(__lowerCAmelCase,		"w",		encoding="utf-8"       ) as f:
                f.write(yaml.dump(__lowerCAmelCase,		allow_unicode=__lowerCAmelCase       )       )
        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_											=						argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  a_											=						parser.parse_args()

  check_model_doc(args.fix_and_overwrite)






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




