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_     :			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,
     )




import math



class 					snake_case__       :



   def __init__(				self   :   int			,	A__   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			Tuple       =       len(A__   )
         snake_case_     :			Union[str, Any]       =       [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1			,	0			,	self.N - 1   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Tuple   )							->     Optional[int]:




         '''simple docstring'''




         return idx * 2



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   List[str]   )							->     Dict:




         '''simple docstring'''




         return idx * 2 + 1



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   Any			,	A__   :   Optional[int]			,	A__   :   Tuple   )							->     List[str]:  # noqa: E741




         '''simple docstring'''




         if l == r:
               snake_case_     :			str       =       A[l]
         else:
               snake_case_     :			List[str]       =       (l + r) // 2
               self.build(self.left(A__   )			,	A__			,	A__   )
               self.build(self.right(A__   )			,	mid + 1			,	A__   )
               snake_case_     :			Union[str, Any]       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Union[str, Any]			,	A__   :   str   )							->     int:




         '''simple docstring'''




         return self.update_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   List[Any]			,	A__   :   str   )							->     Any:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return True
         if l == r:
               snake_case_     :			List[str]       =       val
               return True
         snake_case_     :			Dict       =       (l + r) // 2
         self.update_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__			,	A__   )
         self.update_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Tuple       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )
         return True



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[Any]			,	A__   :   Optional[Any]   )							->     str:




         '''simple docstring'''




         return self.query_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1   )



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   Any			,	A__   :   int			,	A__   :   Tuple			,	A__   :   Tuple			,	A__   :   Dict   )							->     str:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return -math.inf
         if l >= a and r <= b:
               return self.st[idx]
         snake_case_     :			Dict       =       (l + r) // 2
         snake_case_     :			Optional[Any]       =       self.query_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Any       =       self.query_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__   )
         return max(A__			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			int       =       []
         for i in range(1			,	N + 1   ):
               show_list += [self.query(A__			,	A__   )]
         print(A__   )


if __name__ == "__main__":
     UpperCAmelCase							=       [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
     UpperCAmelCase							=       1_5
     UpperCAmelCase							=       SegmentTree(A)
     print(segt.query(4, 6))
     print(segt.query(7, 1_1))
     print(segt.query(7, 1_2))
     segt.update(1, 3, 1_1_1)
     print(segt.query(1, 1_5))
     segt.update(7, 8, 2_3_5)
     segt.show_data()



