






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

 lowerCAmelCase_	:    Any            =      '''0.12'''  # assumed parallelism: 8





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



  @classmethod
  def 		UpperCamelCase_				(      cls				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TOKEN
         HfFolder.save_token(__A				)



  @classmethod
  def 		UpperCamelCase_				(      cls				)			->			List[Any]:



         """simple docstring"""





         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 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			BertConfig(
             vocab_size=99 ,		hidden_size=32 ,		num_hidden_layers=5 ,		num_attention_heads=4 ,		intermediate_size=37				)
         UpperCAmelCase	    =			FlaxBertModel(__A				)
         model.push_to_hub("""test-model-flax""" ,		use_auth_token=self._token				)

         UpperCAmelCase	    =			FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax'''				)

         UpperCAmelCase	    =			flatten_dict(unfreeze(model.params				)				)
         UpperCAmelCase	    =			flatten_dict(unfreeze(new_model.params				)				)

         for key in base_params.keys():
                UpperCAmelCase	    =			(base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(__A ,		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(__A ,		repo_id="""test-model-flax""" ,		push_to_hub=__A ,		use_auth_token=self._token				)

         UpperCAmelCase	    =			FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax'''				)

         UpperCAmelCase	    =			flatten_dict(unfreeze(model.params				)				)
         UpperCAmelCase	    =			flatten_dict(unfreeze(new_model.params				)				)

         for key in base_params.keys():
                UpperCAmelCase	    =			(base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(__A ,		1e-3 ,		msg=f'''{key} not identical'''				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			BertConfig(
             vocab_size=99 ,		hidden_size=32 ,		num_hidden_layers=5 ,		num_attention_heads=4 ,		intermediate_size=37				)
         UpperCAmelCase	    =			FlaxBertModel(__A				)
         model.push_to_hub("""valid_org/test-model-flax-org""" ,		use_auth_token=self._token				)

         UpperCAmelCase	    =			FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""				)

         UpperCAmelCase	    =			flatten_dict(unfreeze(model.params				)				)
         UpperCAmelCase	    =			flatten_dict(unfreeze(new_model.params				)				)

         for key in base_params.keys():
                UpperCAmelCase	    =			(base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(__A ,		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(
                    __A ,		repo_id="""valid_org/test-model-flax-org""" ,		push_to_hub=__A ,		use_auth_token=self._token				)

         UpperCAmelCase	    =			FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""				)

         UpperCAmelCase	    =			flatten_dict(unfreeze(model.params				)				)
         UpperCAmelCase	    =			flatten_dict(unfreeze(new_model.params				)				)

         for key in base_params.keys():
                UpperCAmelCase	    =			(base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(__A ,		1e-3 ,		msg=f'''{key} not identical'''				)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			True
       UpperCAmelCase	    =			flatten_dict(modela.params		)
       UpperCAmelCase	    =			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:
                     UpperCAmelCase	    =			False

       return models_are_equal





@require_flax
class   UpperCamelCase_ (       unittest.TestCase  ):



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)
         UpperCAmelCase	    =			FlaxBertModel(__A				)

         UpperCAmelCase	    =			"""bert"""
         with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(os.path.join(__A ,		__A				)				)

                with self.assertRaises(__A				):
                       UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A				)

                UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A ,		subfolder=__A				)

         self.assertTrue(check_models_equal(__A ,		__A				)				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)
         UpperCAmelCase	    =			FlaxBertModel(__A				)

         UpperCAmelCase	    =			"""bert"""
         with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(os.path.join(__A ,		__A				) ,		max_shard_size="""10KB"""				)

                with self.assertRaises(__A				):
                       UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A				)

                UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A ,		subfolder=__A				)

         self.assertTrue(check_models_equal(__A ,		__A				)				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			"""bert"""
         UpperCAmelCase	    =			"""hf-internal-testing/tiny-random-bert-subfolder"""

         with self.assertRaises(__A				):
                UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A				)

         UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A ,		subfolder=__A				)

         self.assertIsNotNone(__A				)



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			"""bert"""
         UpperCAmelCase	    =			"""hf-internal-testing/tiny-random-bert-sharded-subfolder"""
         with self.assertRaises(__A				):
                UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A				)

         UpperCAmelCase	    =			FlaxBertModel.from_pretrained(__A ,		subfolder=__A				)

         self.assertIsNotNone(__A				)








"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_	:    Optional[Any]            =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
