




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__A						:		int          =						get_logger(__name__)





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				if text.startswith(UpperCamelCase__   ):
								return text[len(UpperCamelCase__   ) :]
				return text  # or whatever





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				return {remove_prefix(UpperCamelCase__						,			"""model."""   ): v for k, v in sd.items()}





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  {}
				for k in state_dicts[0].keys():
								__lowerCAmelCase					=  [sd[k] for sd in state_dicts]
								__lowerCAmelCase					=  sum(UpperCamelCase__   ) / len(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__						,			torch.Tensor   )
								__lowerCAmelCase					=  new_t
				return new_sd





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     None:







				'''simple docstring'''


				__lowerCAmelCase					=  AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__   )
				if os.path.isfile(UpperCamelCase__   ):
								__lowerCAmelCase					=  [pl_ckpt_path]
				else:
								assert os.path.isdir(UpperCamelCase__   )
								__lowerCAmelCase					=  list(Path(UpperCamelCase__   ).glob("""*.ckpt"""   )   )
								assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

				if len(UpperCamelCase__   ) > 1:
								logger.info(F'''averaging the weights of {ckpt_files}'''   )

				__lowerCAmelCase					=  [sanitize(torch.load(UpperCamelCase__						,			map_location="""cpu"""   )["""state_dict"""]   ) for x in ckpt_files]
				__lowerCAmelCase					=  average_state_dicts(UpperCamelCase__   )

				__lowerCAmelCase	,				__lowerCAmelCase					=  hf_model.load_state_dict(UpperCamelCase__						,			strict=UpperCamelCase__   )
				assert not missing, F'''missing keys: {missing}'''
				hf_model.save_pretrained(UpperCamelCase__   )
				try:
								__lowerCAmelCase					=  AutoTokenizer.from_pretrained(UpperCamelCase__   )
								tok.save_pretrained(UpperCamelCase__   )
				except Exception:
								pass
								# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)









'''simple docstring'''




import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor





def 				a_							(    )      ->List[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[Any]           =       set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								__lowerCamelCase		:   Dict           =       getattr(transformers.models.auto				,    lowercase				,    lowercase     )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								__lowerCamelCase		:   Any           =       [
								    x
								    for x in dir(lowercase     )
								    if x.endswith("_MAPPING_NAMES"     )
								    and (x.startswith("MODEL_"     ) or x.startswith("TF_MODEL_"     ) or x.startswith("FLAX_MODEL_"     ))
								]
								for name in mapping_names:
												__lowerCamelCase		:   Union[str, Any]           =       getattr(lowercase				,    lowercase     )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(lowercase				,    (list, tuple)     ):
																								model_names.update(lowercase     )
																				elif isinstance(lowercase				,    lowercase     ):
																								model_names.add(lowercase     )

				return sorted(lowercase     )





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Tuple           =       set(get_all_model_names()     )

				with open("tests/utils/tiny_model_summary.json"     ) as fp:
								__lowerCamelCase		:   Optional[int]           =       json.load(lowercase     )
				__lowerCamelCase		:   Union[str, Any]           =       set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]     )

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				__lowerCamelCase		:   List[str]           =       model_names.difference(lowercase     )
				for model_name in copy.copy(lowercase     ):
								if not model_name.startswith("TF"     ) and F'TF{model_name}' in not_on_hub:
												tiny_models_names.remove(lowercase     )
								elif model_name.startswith("TF"     ) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(lowercase     )

				return sorted(lowercase     )





def 				a_							(    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Optional[int]           =       COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				__lowerCamelCase		:   List[Any]           =       get_all_model_names()
				__lowerCamelCase		:   Any           =       hf_api.list_models(
				    filter=ModelFilter(
				        author="hf-internal-testing"				,    )     )
				__lowerCamelCase		:   str           =       set()
				for x in models:
								__lowerCamelCase		:   Any           =       x.modelId
								__lowerCamelCase    ,       __lowerCamelCase		:   List[Any]           =       model.split("/"     )
								if not model.startswith("tiny-random-"     ):
												continue
								__lowerCamelCase		:   int           =       model.replace("tiny-random-"				,    ""     )
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(lowercase     )

				__lowerCamelCase		:   Tuple           =       sorted(_models     )
				# All tiny model names on Hub
				__lowerCamelCase		:   Union[str, Any]           =       {}
				for model in models:
								__lowerCamelCase		:   List[str]           =       F'hf-internal-testing/tiny-random-{model}'
								__lowerCamelCase		:   Any           =       model.split("-"     )[0]
								try:
												__lowerCamelCase		:   str           =       hf_api.repo_info(lowercase     )
												__lowerCamelCase		:   str           =       {
												    "tokenizer_classes": set(),
												    "processor_classes": set(),
												    "model_classes": set(),
												    "sha": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1     )
												__lowerCamelCase		:   Optional[int]           =       AutoTokenizer.from_pretrained(lowercase     )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   List[Any]           =       AutoTokenizer.from_pretrained(lowercase				,    use_fast=lowercase     )
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   List[Any]           =       AutoImageProcessor.from_pretrained(lowercase     )
												content["processor_classes"].add(img_p.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   Tuple           =       AutoFeatureExtractor.from_pretrained(lowercase     )
												if not isinstance(lowercase				,    lowercase     ):
																content["processor_classes"].add(feat_p.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   Optional[Any]           =       getattr(lowercase				,    lowercase     )
												__lowerCamelCase		:   Optional[Any]           =       model_class.from_pretrained(lowercase     )
												content["model_classes"].add(m.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												__lowerCamelCase		:   List[str]           =       getattr(lowercase				,    F'TF{model}'     )
												__lowerCamelCase		:   Dict           =       model_class.from_pretrained(lowercase     )
												content["model_classes"].add(m.__class__.__name__     )
								except Exception:
												pass

								__lowerCamelCase		:   List[Any]           =       sorted(content["tokenizer_classes"]     )
								__lowerCamelCase		:   str           =       sorted(content["processor_classes"]     )
								__lowerCamelCase		:   Union[str, Any]           =       sorted(content["model_classes"]     )

								__lowerCamelCase		:   Tuple           =       content
								with open(os.path.join(lowercase				,    "hub_tiny_model_summary.json"     )				,    "w"     ) as fp:
												json.dump(lowercase				,    lowercase				,    ensure_ascii=lowercase				,    indent=4     )


if __name__ == "__main__":
				__lowercase	:   Any   		=					argparse.ArgumentParser()
				parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
				__lowercase	:   Optional[int]   		=					parser.parse_args()

				# This has to be `spawn` to avoid hanging forever!
				multiprocessing.set_start_method("""spawn""")

				__lowercase	:   str   		=					"""tiny_models"""
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Optional[int]   		=					None
				__lowercase	:   List[Any]   		=					get_tiny_model_names_from_repo()
				__lowercase	:   List[Any]   		=					True
				__lowercase	:   Dict   		=					True
				__lowercase	:   Tuple   		=					"""hf-internal-testing"""

				create_tiny_models(
				    output_path,
				    all,
				    model_types,
				    models_to_skip,
				    no_check,
				    upload,
				    organization,
				    token=os.environ.get("""TOKEN""", None),
				    num_workers=args.num_workers,
				)





