






'''simple docstring'''
import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
 import os

 from flax.core.frozen_dict import unfreeze
 from flax.traverse_util import flatten_dict

 from transformers import FlaxBertModel

 a_					= '''0.12'''  # assumed parallelism: 8






@require_flax
@is_staging_test
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @classmethod
 def      __magic_name__	(      cls	:							Tuple      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TOKEN
       HfFolder.save_token(snake_case__      )




 @classmethod
 def      __magic_name__	(      cls	:							List[Any]      )	->						Optional[Any]:
       try:
             delete_repo(token=cls._token					,      repo_id='''test-model-flax'''      )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token					,      repo_id='''valid_org/test-model-flax-org'''      )
       except HTTPError:
             pass




 def      __magic_name__	(      self	:							List[str]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Dict									=BertConfig(
           vocab_size=99					,      hidden_size=32					,      num_hidden_layers=5					,      num_attention_heads=4					,      intermediate_size=37      )
       SCREAMING_SNAKE_CASE__     :				Any									=FlaxBertModel(snake_case__      )
       model.push_to_hub('''test-model-flax'''					,      use_auth_token=self._token      )

       SCREAMING_SNAKE_CASE__     :				str									=FlaxBertModel.from_pretrained(F"{USER}/test-model-flax"      )

       SCREAMING_SNAKE_CASE__     :				Dict									=flatten_dict(unfreeze(model.params      )      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=flatten_dict(unfreeze(new_model.params      )      )

       for key in base_params.keys():
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=(base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(snake_case__					,      1e-3					,      msg=F"{key} not identical"      )

       # Reset repo
       delete_repo(token=self._token					,      repo_id='''test-model-flax'''      )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(snake_case__					,      repo_id='''test-model-flax'''					,      push_to_hub=snake_case__					,      use_auth_token=self._token      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=FlaxBertModel.from_pretrained(F"{USER}/test-model-flax"      )

       SCREAMING_SNAKE_CASE__     :				str									=flatten_dict(unfreeze(model.params      )      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=flatten_dict(unfreeze(new_model.params      )      )

       for key in base_params.keys():
             SCREAMING_SNAKE_CASE__     :				str									=(base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(snake_case__					,      1e-3					,      msg=F"{key} not identical"      )




 def      __magic_name__	(      self	:							Tuple      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				str									=BertConfig(
           vocab_size=99					,      hidden_size=32					,      num_hidden_layers=5					,      num_attention_heads=4					,      intermediate_size=37      )
       SCREAMING_SNAKE_CASE__     :				str									=FlaxBertModel(snake_case__      )
       model.push_to_hub('''valid_org/test-model-flax-org'''					,      use_auth_token=self._token      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=flatten_dict(unfreeze(model.params      )      )
       SCREAMING_SNAKE_CASE__     :				Any									=flatten_dict(unfreeze(new_model.params      )      )

       for key in base_params.keys():
             SCREAMING_SNAKE_CASE__     :				Any									=(base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(snake_case__					,      1e-3					,      msg=F"{key} not identical"      )

       # Reset repo
       delete_repo(token=self._token					,      repo_id='''valid_org/test-model-flax-org'''      )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(
                 snake_case__					,      repo_id='''valid_org/test-model-flax-org'''					,      push_to_hub=snake_case__					,      use_auth_token=self._token      )

       SCREAMING_SNAKE_CASE__     :				str									=FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=flatten_dict(unfreeze(model.params      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=flatten_dict(unfreeze(new_model.params      )      )

       for key in base_params.keys():
             SCREAMING_SNAKE_CASE__     :				Any									=(base_params[key] - new_params[key]).sum().item()
             self.assertLessEqual(snake_case__					,      1e-3					,      msg=F"{key} not identical"      )





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Dict									=True
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=flatten_dict(modela.params							)
      SCREAMING_SNAKE_CASE__     :				str									=flatten_dict(modela.params							)
      for key in flat_params_a.keys():
            if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]							)							) > 1e-4:
                  SCREAMING_SNAKE_CASE__     :				int									=False

      return models_are_equal







@require_flax
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							Any      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=FlaxBertModel(snake_case__      )

       SCREAMING_SNAKE_CASE__     :				Any									='''bert'''
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(os.path.join(snake_case__					,      snake_case__      )      )

             with self.assertRaises(snake_case__      ):
                   SCREAMING_SNAKE_CASE__     :				int									=FlaxBertModel.from_pretrained(snake_case__      )

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=FlaxBertModel.from_pretrained(snake_case__					,      subfolder=snake_case__      )

       self.assertTrue(check_models_equal(snake_case__					,      snake_case__      )      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=FlaxBertModel(snake_case__      )

       SCREAMING_SNAKE_CASE__     :				Any									='''bert'''
       with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(os.path.join(snake_case__					,      snake_case__      )					,      max_shard_size='''10KB'''      )

             with self.assertRaises(snake_case__      ):
                   SCREAMING_SNAKE_CASE__     :				Any									=FlaxBertModel.from_pretrained(snake_case__      )

             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=FlaxBertModel.from_pretrained(snake_case__					,      subfolder=snake_case__      )

       self.assertTrue(check_models_equal(snake_case__					,      snake_case__      )      )




 def      __magic_name__	(      self	:							Tuple      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									='''bert'''
       SCREAMING_SNAKE_CASE__     :				str									='''hf-internal-testing/tiny-random-bert-subfolder'''

       with self.assertRaises(snake_case__      ):
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=FlaxBertModel.from_pretrained(snake_case__      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=FlaxBertModel.from_pretrained(snake_case__					,      subfolder=snake_case__      )

       self.assertIsNotNone(snake_case__      )




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Tuple									='''bert'''
       SCREAMING_SNAKE_CASE__     :				Optional[int]									='''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
       with self.assertRaises(snake_case__      ):
             SCREAMING_SNAKE_CASE__     :				List[Any]									=FlaxBertModel.from_pretrained(snake_case__      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=FlaxBertModel.from_pretrained(snake_case__					,      subfolder=snake_case__      )

       self.assertIsNotNone(snake_case__      )







'''simple docstring'''
from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

a_					= TypeVar('T')






class      __SCREAMING_SNAKE_CASE      (			Generic[T]				):
 snake_case_  							=							42  # Cache store of keys
 snake_case_  							=							42  # References of the keys in cache
 snake_case_  							=							10  # Maximum capacity of cache




 def __init__(      self	:							Dict					,      __lowercase	:							int      )	->						None:
       SCREAMING_SNAKE_CASE__     :				Any									=deque()
       SCREAMING_SNAKE_CASE__     :				str									=set()
       if not n:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=sys.maxsize
       elif n < 0:
             raise ValueError('''n should be an integer greater than 0.'''      )
       else:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=n




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							T      )	->						None:
       if x not in self.key_reference:
             if len(self.dq_store      ) == LRUCache._MAX_CAPACITY:
                   SCREAMING_SNAKE_CASE__     :				int									=self.dq_store.pop()
                   self.key_reference.remove(__lowercase      )
       else:
             self.dq_store.remove(__lowercase      )

       self.dq_store.appendleft(__lowercase      )
       self.key_reference.add(__lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						None:
       for k in self.dq_store:
             print(__lowercase      )




 def __repr__(      self	:							List[Any]      )	->						str:
       return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store      )}"


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_					= LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"