


"""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

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


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

     '''simple docstring'''


     @classmethod
     def 							_UpperCAmelCase      (      cls							)       ->							Optional[Any]:
          _a    	=				TOKEN
          HfFolder.save_token(__A							)


     @classmethod
     def 							_UpperCAmelCase      (      cls							)       ->							List[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 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				BertConfig(
              vocab_size=99    ,				hidden_size=32    ,				num_hidden_layers=5    ,				num_attention_heads=4    ,				intermediate_size=37							)
          _a    	=				FlaxBertModel(__A							)
          model.push_to_hub('''test-model-flax'''    ,				use_auth_token=self._token							)

          _a    	=				FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'							)

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

          for key in base_params.keys():
               _a    	=				(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							)

          _a    	=				FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'							)

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

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





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

          _a    	=				FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''							)

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

          for key in base_params.keys():
               _a    	=				(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							)

          _a    	=				FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''							)

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

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







def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				True
     _a    	=				flatten_dict(modela.params       )
     _a    	=				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:
               _a    	=				False

     return models_are_equal


@require_flax
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''							)
          _a    	=				FlaxBertModel(__A							)

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

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

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

          self.assertTrue(check_models_equal(__A    ,				__A							)							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''							)
          _a    	=				FlaxBertModel(__A							)

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

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

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

          self.assertTrue(check_models_equal(__A    ,				__A							)							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				'''bert'''
          _a    	=				'''hf-internal-testing/tiny-random-bert-subfolder'''

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

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

          self.assertIsNotNone(__A							)





     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				'''bert'''
          _a    	=				'''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
          with self.assertRaises(__A							):
               _a    	=				FlaxBertModel.from_pretrained(__A							)

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

          self.assertIsNotNone(__A							)



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case		=       {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViTMAEForPreTraining''',
       '''ViTMAELayer''',
       '''ViTMAEModel''',
       '''ViTMAEPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''TFViTMAEForPreTraining''',
       '''TFViTMAEModel''',
       '''TFViTMAEPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)