


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 			_lowerCAmelCase						(							)->Tuple:
   '''simple docstring'''






   snake_case_					    =					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_					    =					getattr(transformers.models.auto		,						snake_case_		,						snake_case_							)
      if module is None:
         continue
      # all mappings in a single auto modeling file
      snake_case_					    =					[
          x
          for x in dir(snake_case_							)
          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_					    =					getattr(snake_case_		,						snake_case_							)
         if mapping is not None:
            for v in mapping.values():
               if isinstance(snake_case_		,						(list, tuple)							):
                  model_names.update(snake_case_							)
               elif isinstance(snake_case_		,						snake_case_							):
                  model_names.add(snake_case_							)

   return sorted(snake_case_							)



def 			_lowerCAmelCase						(							)->Optional[Any]:
   '''simple docstring'''






   snake_case_					    =					set(get_all_model_names()							)

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

   return sorted(snake_case_							)



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Tuple							)->Tuple:
   '''simple docstring'''






   snake_case_					    =					COMPOSITE_MODELS.values()

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

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

      snake_case_					    =					sorted(content["tokenizer_classes"]							)
      snake_case_					    =					sorted(content["processor_classes"]							)
      snake_case_					    =					sorted(content["model_classes"]							)

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


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

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

   SCREAMING_SNAKE_CASE	:Any  		=     "tiny_models"
   SCREAMING_SNAKE_CASE	:Optional[int]  		=     True
   SCREAMING_SNAKE_CASE	:int  		=     None
   SCREAMING_SNAKE_CASE	:str  		=     get_tiny_model_names_from_repo()
   SCREAMING_SNAKE_CASE	:List[Any]  		=     True
   SCREAMING_SNAKE_CASE	:List[Any]  		=     True
   SCREAMING_SNAKE_CASE	:Optional[Any]  		=     "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 Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'longt5'
  _SCREAMING_SNAKE_CASE		    =       ['past_key_values']
  _SCREAMING_SNAKE_CASE		    =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

     snake_case_					    =					self.feed_forward_proj.split("-"  )
     snake_case_					    =					act_info[-1]
     snake_case_					    =					act_info[0] == "gated"

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"  )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_					    =					"gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase    ,				eos_token_id=_lowerCAmelCase    ,				is_encoder_decoder=_lowerCAmelCase    ,				**_lowerCAmelCase    ,				)






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase    ,				direction="inputs"  )

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




