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 					SCREAMING_SNAKE_CASE_			(		):
      snake_case_     :			Union[str, 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"]:
            snake_case_     :			Optional[int]       =       getattr(transformers.models.auto ,	lowerCAmelCase_ ,	lowerCAmelCase_     )
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            snake_case_     :			Dict       =       [
                x
                for x in dir(lowerCAmelCase_     )
                if x.endswith("_MAPPING_NAMES"     )
                and (x.startswith("MODEL_"     ) or x.startswith("TF_MODEL_"     ) or x.startswith("FLAX_MODEL_"     ))
            ]
            for name in mapping_names:
                  snake_case_     :			Tuple       =       getattr(lowerCAmelCase_ ,	lowerCAmelCase_     )
                  if mapping is not None:
                        for v in mapping.values():
                              if isinstance(lowerCAmelCase_ ,	(list, tuple)     ):
                                    model_names.update(lowerCAmelCase_     )
                              elif isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     ):
                                    model_names.add(lowerCAmelCase_     )

      return sorted(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		):
      # All model names defined in auto mappings
      snake_case_     :			Dict       =       set(get_all_model_names()     )

      with open("tests/utils/tiny_model_summary.json"     ) as fp:
            snake_case_     :			int       =       json.load(lowerCAmelCase_     )
      snake_case_     :			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.
      snake_case_     :			Optional[int]       =       model_names.difference(lowerCAmelCase_     )
      for model_name in copy.copy(lowerCAmelCase_     ):
            if not model_name.startswith("TF"     ) and f"TF{model_name}" in not_on_hub:
                  tiny_models_names.remove(lowerCAmelCase_     )
            elif model_name.startswith("TF"     ) and model_name[2:] in not_on_hub:
                  tiny_models_names.remove(lowerCAmelCase_     )

      return sorted(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict     ):
      snake_case_     :			str       =       COMPOSITE_MODELS.values()

      # All tiny model base names on Hub
      snake_case_     :			Optional[int]       =       get_all_model_names()
      snake_case_     :			Tuple       =       hf_api.list_models(
          filter=ModelFilter(
              author="hf-internal-testing" ,	)     )
      snake_case_     :			str       =       set()
      for x in models:
            snake_case_     :			Tuple       =       x.modelId
            snake_case_  ,snake_case_     :			Dict       =       model.split("/"     )
            if not model.startswith("tiny-random-"     ):
                  continue
            snake_case_     :			Optional[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(lowerCAmelCase_     )

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

            snake_case_     :			List[str]       =       sorted(content["tokenizer_classes"]     )
            snake_case_     :			Dict       =       sorted(content["processor_classes"]     )
            snake_case_     :			List[str]       =       sorted(content["model_classes"]     )

            snake_case_     :			Optional[Any]       =       content
            with open(os.path.join(lowerCAmelCase_ ,	"hub_tiny_model_summary.json"     ) ,	"w"     ) as fp:
                  json.dump(lowerCAmelCase_ ,	lowerCAmelCase_ ,	ensure_ascii=lowerCAmelCase_ ,	indent=4     )


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

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

     UpperCAmelCase							=       "tiny_models"
     UpperCAmelCase							=       True
     UpperCAmelCase							=       None
     UpperCAmelCase							=       get_tiny_model_names_from_repo()
     UpperCAmelCase							=       True
     UpperCAmelCase							=       True
     UpperCAmelCase							=       "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,
     )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



